Open-Meteo API data Analysis (Nashville, TN)

# Open-Meteo API data pull

# Notes :
# Location to be analyzed, located at similar Latitude and geographics, but has very different weather (rain precipitation):
#    a. Las Vegas (dry), coord: 36.1716° N, 115.1391° W
#    b. Nashville (wet), coord: 36.1627° N, 86.7816° W
###############################################################################################
import openmeteo_requests
import requests_cache
from retry_requests import retry
import pandas as pd

# Setup the Open-Meteo API client with cache and retry on error
cache_session = requests_cache.CachedSession('.cache', expire_after = 3600)
retry_session = retry(cache_session, retries = 5, backoff_factor = 0.2)
openmeteo = openmeteo_requests.Client(session = retry_session)

# Make sure all required weather variables are listed here
# The order of variables in hourly or daily is important to assign them correctly below
url = "https://api.open-meteo.com/v1/forecast"
params = {
	"latitude": 36.1659,
	"longitude": -86.7844,
	"hourly": ["temperature_2m", "relative_humidity_2m", "dew_point_2m", "apparent_temperature", "precipitation_probability", "precipitation", "rain", "showers", "pressure_msl", "surface_pressure", "cloud_cover", "cloud_cover_low", "cloud_cover_mid", "cloud_cover_high", "visibility", "wind_speed_10m", "soil_moisture_0_to_1cm", "soil_moisture_1_to_3cm", "soil_moisture_3_to_9cm", "soil_moisture_9_to_27cm", "soil_moisture_27_to_81cm", "evapotranspiration", "snowfall", "snow_depth", "et0_fao_evapotranspiration", "vapour_pressure_deficit", "wind_speed_80m", "wind_speed_120m", "wind_speed_180m", "wind_direction_10m", "wind_direction_80m", "wind_direction_120m", "wind_direction_180m", "temperature_80m", "temperature_120m", "temperature_180m", "soil_temperature_0cm", "soil_temperature_6cm", "soil_temperature_18cm", "soil_temperature_54cm", "wind_gusts_10m"],
	"past_days": 92,
	"forecast_days": 1
}
responses = openmeteo.weather_api(url, params=params)

# Process first location. Add a for-loop for multiple locations or weather models
response = responses[0]
print(f"Coordinates {response.Latitude()}°N {response.Longitude()}°E")
print(f"Elevation {response.Elevation()} m asl")
print(f"Timezone {response.Timezone()}{response.TimezoneAbbreviation()}")
print(f"Timezone difference to GMT+0 {response.UtcOffsetSeconds()} s")

# Process hourly data. The order of variables needs to be the same as requested.
hourly = response.Hourly()
hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()
hourly_relative_humidity_2m = hourly.Variables(1).ValuesAsNumpy()
hourly_dew_point_2m = hourly.Variables(2).ValuesAsNumpy()
hourly_apparent_temperature = hourly.Variables(3).ValuesAsNumpy()
hourly_precipitation_probability = hourly.Variables(4).ValuesAsNumpy()
hourly_precipitation = hourly.Variables(5).ValuesAsNumpy()
hourly_rain = hourly.Variables(6).ValuesAsNumpy()
hourly_showers = hourly.Variables(7).ValuesAsNumpy()
hourly_pressure_msl = hourly.Variables(8).ValuesAsNumpy()
hourly_surface_pressure = hourly.Variables(9).ValuesAsNumpy()
hourly_cloud_cover = hourly.Variables(10).ValuesAsNumpy()
hourly_cloud_cover_low = hourly.Variables(11).ValuesAsNumpy()
hourly_cloud_cover_mid = hourly.Variables(12).ValuesAsNumpy()
hourly_cloud_cover_high = hourly.Variables(13).ValuesAsNumpy()
hourly_visibility = hourly.Variables(14).ValuesAsNumpy()
hourly_wind_speed_10m = hourly.Variables(15).ValuesAsNumpy()
hourly_soil_moisture_0_to_1cm = hourly.Variables(16).ValuesAsNumpy()
hourly_soil_moisture_1_to_3cm = hourly.Variables(17).ValuesAsNumpy()
hourly_soil_moisture_3_to_9cm = hourly.Variables(18).ValuesAsNumpy()
hourly_soil_moisture_9_to_27cm = hourly.Variables(19).ValuesAsNumpy()
hourly_soil_moisture_27_to_81cm = hourly.Variables(20).ValuesAsNumpy()
hourly_evapotranspiration = hourly.Variables(21).ValuesAsNumpy()
hourly_snowfall = hourly.Variables(22).ValuesAsNumpy()
hourly_snow_depth = hourly.Variables(23).ValuesAsNumpy()
hourly_et0_fao_evapotranspiration = hourly.Variables(24).ValuesAsNumpy()
hourly_vapour_pressure_deficit = hourly.Variables(25).ValuesAsNumpy()
hourly_wind_speed_80m = hourly.Variables(26).ValuesAsNumpy()
hourly_wind_speed_120m = hourly.Variables(27).ValuesAsNumpy()
hourly_wind_speed_180m = hourly.Variables(28).ValuesAsNumpy()
hourly_wind_direction_10m = hourly.Variables(29).ValuesAsNumpy()
hourly_wind_direction_80m = hourly.Variables(30).ValuesAsNumpy()
hourly_wind_direction_120m = hourly.Variables(31).ValuesAsNumpy()
hourly_wind_direction_180m = hourly.Variables(32).ValuesAsNumpy()
hourly_temperature_80m = hourly.Variables(33).ValuesAsNumpy()
hourly_temperature_120m = hourly.Variables(34).ValuesAsNumpy()
hourly_temperature_180m = hourly.Variables(35).ValuesAsNumpy()
hourly_soil_temperature_0cm = hourly.Variables(36).ValuesAsNumpy()
hourly_soil_temperature_6cm = hourly.Variables(37).ValuesAsNumpy()
hourly_soil_temperature_18cm = hourly.Variables(38).ValuesAsNumpy()
hourly_soil_temperature_54cm = hourly.Variables(39).ValuesAsNumpy()
hourly_wind_gusts_10m = hourly.Variables(40).ValuesAsNumpy()

hourly_data = {"date": pd.date_range(
	start = pd.to_datetime(hourly.Time(), unit = "s", utc = True),
	end = pd.to_datetime(hourly.TimeEnd(), unit = "s", utc = True),
	freq = pd.Timedelta(seconds = hourly.Interval()),
	inclusive = "left"
)}

hourly_data["temperature_2m"] = hourly_temperature_2m
hourly_data["relative_humidity_2m"] = hourly_relative_humidity_2m
hourly_data["dew_point_2m"] = hourly_dew_point_2m
hourly_data["apparent_temperature"] = hourly_apparent_temperature
hourly_data["precipitation_probability"] = hourly_precipitation_probability
hourly_data["precipitation"] = hourly_precipitation
hourly_data["rain"] = hourly_rain
hourly_data["showers"] = hourly_showers
hourly_data["pressure_msl"] = hourly_pressure_msl
hourly_data["surface_pressure"] = hourly_surface_pressure
hourly_data["cloud_cover"] = hourly_cloud_cover
hourly_data["cloud_cover_low"] = hourly_cloud_cover_low
hourly_data["cloud_cover_mid"] = hourly_cloud_cover_mid
hourly_data["cloud_cover_high"] = hourly_cloud_cover_high
hourly_data["visibility"] = hourly_visibility
hourly_data["wind_speed_10m"] = hourly_wind_speed_10m
hourly_data["soil_moisture_0_to_1cm"] = hourly_soil_moisture_0_to_1cm
hourly_data["soil_moisture_1_to_3cm"] = hourly_soil_moisture_1_to_3cm
hourly_data["soil_moisture_3_to_9cm"] = hourly_soil_moisture_3_to_9cm
hourly_data["soil_moisture_9_to_27cm"] = hourly_soil_moisture_9_to_27cm
hourly_data["soil_moisture_27_to_81cm"] = hourly_soil_moisture_27_to_81cm
hourly_data["evapotranspiration"] = hourly_evapotranspiration
hourly_data["snowfall"] = hourly_snowfall
hourly_data["snow_depth"] = hourly_snow_depth
hourly_data["et0_fao_evapotranspiration"] = hourly_et0_fao_evapotranspiration
hourly_data["vapour_pressure_deficit"] = hourly_vapour_pressure_deficit
hourly_data["wind_speed_80m"] = hourly_wind_speed_80m
hourly_data["wind_speed_120m"] = hourly_wind_speed_120m
hourly_data["wind_speed_180m"] = hourly_wind_speed_180m
hourly_data["wind_direction_10m"] = hourly_wind_direction_10m
hourly_data["wind_direction_80m"] = hourly_wind_direction_80m
hourly_data["wind_direction_120m"] = hourly_wind_direction_120m
hourly_data["wind_direction_180m"] = hourly_wind_direction_180m
hourly_data["temperature_80m"] = hourly_temperature_80m
hourly_data["temperature_120m"] = hourly_temperature_120m
hourly_data["temperature_180m"] = hourly_temperature_180m
hourly_data["soil_temperature_0cm"] = hourly_soil_temperature_0cm
hourly_data["soil_temperature_6cm"] = hourly_soil_temperature_6cm
hourly_data["soil_temperature_18cm"] = hourly_soil_temperature_18cm
hourly_data["soil_temperature_54cm"] = hourly_soil_temperature_54cm
hourly_data["wind_gusts_10m"] = hourly_wind_gusts_10m

hourly_dataframe = pd.DataFrame(data = hourly_data)
print(hourly_dataframe)

Coordinates 36.16115188598633°N -86.79808807373047°E
Elevation 158.0 m asl
Timezone NoneNone
Timezone difference to GMT+0 0 s
                          date  temperature_2m  relative_humidity_2m  \
0    2025-03-01 00:00:00+00:00             NaN                   NaN   
1    2025-03-01 01:00:00+00:00             NaN                   NaN   
2    2025-03-01 02:00:00+00:00             NaN                   NaN   
3    2025-03-01 03:00:00+00:00             NaN                   NaN   
4    2025-03-01 04:00:00+00:00             NaN                   NaN   
...                        ...             ...                   ...   
2227 2025-06-01 19:00:00+00:00       27.602501                  45.0   
2228 2025-06-01 20:00:00+00:00       28.002501                  45.0   
2229 2025-06-01 21:00:00+00:00       28.152500                  44.0   
2230 2025-06-01 22:00:00+00:00       27.902500                  45.0   
2231 2025-06-01 23:00:00+00:00       27.252501                  47.0   

      dew_point_2m  apparent_temperature  precipitation_probability  \
0              NaN                   NaN                        0.0   
1              NaN                   NaN                        0.0   
2              NaN                   NaN                        0.0   
3              NaN                   NaN                        0.0   
4              NaN                   NaN                        0.0   
...            ...                   ...                        ...   
2227     14.604142             28.444843                        8.0   
2228     14.966615             28.638031                        6.0   
2229     14.753403             28.290123                        3.0   
2230     14.876000             27.838562                        4.0   
2231     14.960908             27.168846                        9.0   

      precipitation  rain  showers  pressure_msl  surface_pressure  \
0               NaN   NaN      NaN           NaN               NaN   
1               NaN   NaN      NaN           NaN               NaN   
2               NaN   NaN      NaN           NaN               NaN   
3               NaN   NaN      NaN           NaN               NaN   
4               NaN   NaN      NaN           NaN               NaN   
...             ...   ...      ...           ...               ...   
2227            0.0   0.0      0.0   1012.200012        994.226318   
2228            0.0   0.0      0.0   1011.900024        993.955139   
2229            0.0   0.0      0.0   1011.700012        993.767639   
2230            0.0   0.0      0.0   1011.700012        993.752747   
2231            0.0   0.0      0.0   1011.500000        993.517761   

      cloud_cover  cloud_cover_low  cloud_cover_mid  cloud_cover_high  \
0             NaN              NaN              NaN               NaN   
1             NaN              NaN              NaN               NaN   
2             NaN              NaN              NaN               NaN   
3             NaN              NaN              NaN               NaN   
4             NaN              NaN              NaN               NaN   
...           ...              ...              ...               ...   
2227         24.0             24.0              0.0               0.0   
2228         24.0             24.0              0.0               0.0   
2229         23.0             23.0              0.0               0.0   
2230         28.0             28.0              0.0               0.0   
2231          4.0              4.0              0.0               0.0   

      visibility  wind_speed_10m  soil_moisture_0_to_1cm  \
0            NaN             NaN                     NaN   
1            NaN             NaN                     NaN   
2            NaN             NaN                     NaN   
3            NaN             NaN                     NaN   
4            NaN             NaN                     NaN   
...          ...             ...                     ...   
2227     42600.0       12.849528                   0.275   
2228     43400.0       12.574260                   0.274   
2229     44200.0       11.659777                   0.272   
2230     42900.0       11.525623                   0.271   
2231     40600.0       11.885453                   0.271   

      soil_moisture_1_to_3cm  soil_moisture_3_to_9cm  soil_moisture_9_to_27cm  \
0                        NaN                     NaN                      NaN   
1                        NaN                     NaN                      NaN   
2                        NaN                     NaN                      NaN   
3                        NaN                     NaN                      NaN   
4                        NaN                     NaN                      NaN   
...                      ...                     ...                      ...   
2227                   0.280                   0.288                    0.304   
2228                   0.278                   0.287                    0.303   
2229                   0.276                   0.285                    0.303   
2230                   0.275                   0.284                    0.302   
2231                   0.275                   0.283                    0.302   

      soil_moisture_27_to_81cm  evapotranspiration  snowfall  snow_depth  \
0                          NaN                 0.0       NaN         NaN   
1                          NaN                 0.0       NaN         NaN   
2                          NaN                 0.0       NaN         NaN   
3                          NaN                 0.0       NaN         NaN   
4                          NaN                 0.0       NaN         NaN   
...                        ...                 ...       ...         ...   
2227                     0.332                 0.0       0.0         0.0   
2228                     0.332                 0.0       0.0         0.0   
2229                     0.332                 0.0       0.0         0.0   
2230                     0.332                 0.0       0.0         0.0   
2231                     0.332                 0.0       0.0         0.0   

      et0_fao_evapotranspiration  vapour_pressure_deficit  wind_speed_80m  \
0                            NaN                      NaN             NaN   
1                            NaN                      NaN             NaN   
2                            NaN                      NaN             NaN   
3                            NaN                      NaN             NaN   
4                            NaN                      NaN             NaN   
...                          ...                      ...             ...   
2227                    0.576654                 2.030955       15.745627   
2228                    0.537299                 2.078797       15.111424   
2229                    0.473631                 2.135164       14.264361   
2230                    0.396486                 2.066745       14.400000   
2231                    0.279190                 1.917439       15.480000   

      wind_speed_120m  wind_speed_180m  wind_direction_10m  \
0                 NaN              NaN                 NaN   
1                 NaN              NaN                 NaN   
2                 NaN              NaN                 NaN   
3                 NaN              NaN                 NaN   
4                 NaN              NaN                 NaN   
...               ...              ...                 ...   
2227        16.506481        14.830076          258.690094   
2228        18.276533        12.849528          256.759460   
2229        22.435301        11.384199          261.119415   
2230        21.315056        11.269782          271.789856   
2231        21.085789        14.773164          271.735657   

      wind_direction_80m  wind_direction_120m  wind_direction_180m  \
0                    NaN                  NaN                  NaN   
1                    NaN                  NaN                  NaN   
2                    NaN                  NaN                  NaN   
3                    NaN                  NaN                  NaN   
4                    NaN                  NaN                  NaN   
...                  ...                  ...                  ...   
2227          259.460876           286.821503           275.572113   
2228          257.619263           300.173431           281.309906   
2229          259.824554           301.607452           288.435028   
2230          270.000000           305.882233           296.564972   
2231          270.000000           318.532379           313.025085   

      temperature_80m  temperature_120m  temperature_180m  \
0                 NaN               NaN               NaN   
1                 NaN               NaN               NaN   
2                 NaN               NaN               NaN   
3                 NaN               NaN               NaN   
4                 NaN               NaN               NaN   
...               ...               ...               ...   
2227        25.410500         25.210501         23.867500   
2228        25.760500         25.510500         24.267500   
2229        25.260500         25.010500         24.767500   
2230        24.310501         24.110500         25.167501   
2231        23.510500         23.310501         24.867500   

      soil_temperature_0cm  soil_temperature_6cm  soil_temperature_18cm  \
0                      NaN                   NaN                    NaN   
1                      NaN                   NaN                    NaN   
2                      NaN                   NaN                    NaN   
3                      NaN                   NaN                    NaN   
4                      NaN                   NaN                    NaN   
...                    ...                   ...                    ...   
2227             30.217501             26.917501              21.917501   
2228             31.567501             27.067501              22.317501   
2229             29.367500             27.167501              22.717501   
2230             28.267500             26.867500              23.017500   
2231             26.867500             26.467501              23.267500   

      soil_temperature_54cm  wind_gusts_10m  
0                       NaN             NaN  
1                       NaN             NaN  
2                       NaN             NaN  
3                       NaN             NaN  
4                       NaN             NaN  
...                     ...             ...  
2227              19.917501       16.559999  
2228              19.917501       16.199999  
2229              19.917501       14.759999  
2230              19.967501       15.480000  
2231              19.967501       16.559999  

[2232 rows x 42 columns]

# Store the data into the CSV file
path1 = r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\Global_Weather\openmeteo_data_nashville_tn.csv'
hourly_dataframe.to_csv(path1, header='column_names')

Geolocation of Las Vegas, NV and Nashville, TN

# Visualize geolocation by Latitude and Longitude of Las Vegas and Nashville
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20,10)

# Load a world map shapefile (built-in dataset)
world = gpd.read_file(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_admin_0_countries.shp')
worldpop = gpd.read_file(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_populated_places.shp')

#    a. Las Vegas (dry), coord: 36.1716° N, 115.1391° W
#    b. Nashville (wet), coord: 36.1627° N, 86.7816° W

data = {'longitude': [-115.1391, -86.7816],
        'latitude': [36.1716, 36.1627],
        'value': [10, 20]}

df = pd.DataFrame(data)

# Create geometry column
geometry = gpd.points_from_xy(df['longitude'], df['latitude'], crs="EPSG:4326")

# Create GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry=geometry)

print(gdf)

usapop = worldpop[worldpop['ADM0NAME'] == 'United States of America']

# # Plotting multiple layers
fig, ax = plt.subplots()
plt.title('Geolocation of Las Vegas and Nashville')
world.plot(ax = ax, cmap = 'hsv', edgecolor = 'black', column = 'SOVEREIGNT')
gdf.plot(ax = ax, color = 'yellow', markersize = 40)  # The location of Las Vegas and Nashville can be seen as yellow dots in the map

   longitude  latitude  value                   geometry
0  -115.1391   36.1716     10  POINT (-115.1391 36.1716)
1   -86.7816   36.1627     20   POINT (-86.7816 36.1627)

<Axes: title={'center': 'Geolocation of Las Vegas and Nashville'}>

# Visualize Las Vegas (and Nevada) and Nashville (and Tennessee)
# States are depicted with blue color
# Cities are depicted with yellow color
stateprov = gpd.read_file(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_admin_1_states_provinces.shp')

usamain = stateprov[stateprov["name"] != "Alaska"]
usamain = usamain[usamain["name"] != "Hawaii"]
nevada = stateprov[stateprov["name"] == "Nevada"]
tennessee = stateprov[stateprov["name"] == "Tennessee"]

# Add Column 'coords' for labelling
nevada['coords'] = nevada['geometry'].apply(lambda x: x.representative_point().coords[:])
nevada['coords'] = [coords[0] for coords in nevada['coords']]
tennessee['coords'] = tennessee['geometry'].apply(lambda x: x.representative_point().coords[:])
tennessee['coords'] = [coords[0] for coords in tennessee['coords']]

# # Plotting multiple layers
fig, ax = plt.subplots()

for idx, row in nevada.iterrows():
    plt.annotate(text=row['name'], xy=row['coords'],
                 horizontalalignment='center')

for idx, row in tennessee.iterrows():
    plt.annotate(text=row['name'], xy=row['coords'],
                 horizontalalignment='center')
    
usamain.plot(ax = ax, color='green', edgecolor = 'black')
nevada.plot(ax = ax, color = 'blue', markersize = 40)
tennessee.plot(ax = ax, color = 'blue', markersize = 40)
gdf.plot(ax = ax, color = 'yellow', markersize = 40)

C:\ProgramData\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1981: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
C:\ProgramData\anaconda3\Lib\site-packages\geopandas\geodataframe.py:1981: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

<Axes: >

# Maps of Nashville
import pandas as pd
import geopandas as gpd
gdf = gpd.read_file(r"C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\cities.geojson")
nashvilledf = gdf.loc[gdf['NAME'].isin(['NASHVILLE'])]

# Remove all unwanted Nashvilles, just leave the one with Index 23012, Nashville in Tennessee state.
# We use drop Method coz this Method doesn't change the GeoDataframe into Series.
# If we use .iloc or .loc Method, it will change the GeoDataframe into Series.
nashvilledf = nashvilledf.drop(20793)
nashvilledf = nashvilledf.drop(22744)
nashvilledf = nashvilledf.drop(23065)
nashvilledf = nashvilledf.drop(23759)

nashvilledf.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

Open-Meteo Data Analysis

# Read from the CSV file
import pandas as pd
import matplotlib.pyplot as plt
path1 = r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\Global_Weather\openmeteo_data_nashville_tn.csv'
df1 = pd.read_csv(path1)
df1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	...	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m
0	0	2025-02-23 00:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1	2025-02-23 01:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2	2025-02-23 02:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	3	2025-02-23 03:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	4	2025-02-23 04:00:00+00:00	NaN	NaN	NaN	NaN	0.0	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	2227	2025-05-26 19:00:00+00:00	21.5025	76.0	17.093630	23.275127	3.0	0.0	0.0	0.0	...	88.898320	63.904633	21.160500	20.9105	19.767500	25.567501	23.167501	19.467500	19.3675	9.360000
2228	2228	2025-05-26 20:00:00+00:00	21.9025	77.0	17.687681	23.821278	2.0	0.0	0.0	0.0	...	83.774260	67.067870	20.560501	20.3605	20.667501	25.567501	23.267500	19.817501	19.3675	8.640000
2229	2229	2025-05-26 21:00:00+00:00	22.0025	76.0	17.577087	23.358010	3.0	0.0	0.0	0.0	...	85.236440	65.725570	20.210500	20.0105	20.867500	24.367500	23.017500	20.117500	19.3675	12.599999
2230	2230	2025-05-26 22:00:00+00:00	21.5525	75.0	16.932825	22.161495	6.0	0.0	0.0	0.0	...	99.039406	68.629310	19.360500	19.1605	21.167501	24.967500	22.967500	20.367500	19.3675	19.800000
2231	2231	2025-05-26 23:00:00+00:00	20.8025	75.0	16.208752	21.156380	10.0	0.0	0.0	0.0	...	81.416466	70.497470	17.710500	17.6105	20.967500	22.217500	22.667501	20.567501	19.3675	21.240000

2232 rows × 43 columns

# Data Cleaning

# Remove Duplicate data from raw table
df1 = df1.drop_duplicates()

# Drop/remove all entries with blanks
# df.dropna(subset='temperature_2m', inplace=True)
# df.dropna(subset='relative_humidity_2m', inplace=True)
df1.dropna(inplace=True)

# Set to display all columns
pd.set_option('display.max_columns',None)
plt.rcParams['figure.figsize'] = (12, 8)

# Check whether there's still any blank rows/columns
df1[df1.isna().any(axis=1)]

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m

# Add new Columns to assist in analysis

# Add Column 'Location'
df1['Location'] = 'Nashville'

# Split the Date into Year, Month and Date
df1[['ymd','hmstz']] = df1['date'].str.split(' ', expand=True)
df1[['year','month','day']] = df1['ymd'].str.split('-', expand=True)
df1[['hour','minute','sectz','tzsec']] = df1['hmstz'].str.split(':', expand=True)

# Data Analysis

# Find out the correlation among Columns to filter what Columns we'll use for analysis
# For first glance, we filter out by common logic, what Columns are possible to have strong enough correlations.
# We choose a bunch of Columns specified with Index numbers or to be more humanly readable, the Columns name.
df1h = df1[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m',
            'dew_point_2m','vapour_pressure_deficit','temperature_2m','cloud_cover_high','cloud_cover_low','wind_speed_10m',
            'soil_moisture_3_to_9cm','temperature_80m','soil_temperature_0cm']]
df1h

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
581	0.0	0.0	0.233	0.240	53.0	7.163813	0.900061	16.7995	100.0	0.0	19.296133	0.257	17.460500	13.617499
582	0.0	0.0	0.232	0.240	53.0	7.071089	0.894375	16.6995	100.0	0.0	18.792551	0.257	17.510500	14.917500
583	0.0	0.0	0.232	0.240	57.0	7.343195	0.775166	15.8495	100.0	0.0	18.214718	0.256	16.460500	14.517500
584	0.0	0.0	0.232	0.240	59.0	7.379331	0.715844	15.3495	100.0	0.0	18.250260	0.256	15.910501	14.417500
585	0.0	0.0	0.232	0.240	61.0	7.208212	0.650970	14.6495	100.0	0.0	18.643690	0.256	15.160501	13.567500
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	3.0	33.0	0.265	0.269	76.0	17.093630	0.615556	21.5025	90.0	100.0	5.091168	0.275	21.160500	25.567501
2228	2.0	100.0	0.262	0.266	77.0	17.687681	0.604456	21.9025	100.0	98.0	5.860375	0.273	20.560501	25.567501
2229	3.0	100.0	0.261	0.264	76.0	17.577087	0.634595	22.0025	100.0	100.0	9.387651	0.271	20.210500	24.367500
2230	6.0	100.0	0.260	0.263	75.0	16.932825	0.643173	21.5525	100.0	74.0	12.599998	0.270	19.360500	24.967500
2231	10.0	100.0	0.259	0.262	75.0	16.208752	0.614321	20.8025	100.0	71.0	12.287555	0.268	17.710500	22.217500

1651 rows × 14 columns

# Create a Function to generate sorted, limited Correlation Table of a certain Column/parameter
def gencorr(param):
    df1hcorr = df1h.corr(numeric_only=True).abs().sort_values(by=param, ascending=False)
    df1hcorr = df1hcorr.where(df1hcorr[param] > 0.5)
    df1hcorr = df1hcorr.dropna()
    corresult = df1hcorr[param].round(2)
    return corresult

# Generate Correlation Table for 'temperature_2m'
gencorr('temperature_2m') # Koreksi

temperature_2m             1.00
temperature_80m            0.96
soil_temperature_0cm       0.92
vapour_pressure_deficit    0.65
dew_point_2m               0.63
Name: temperature_2m, dtype: float64

# Draw plot of the most correlated columns with 'temperature_2m'
# For the more than half part of the Plot, the fluctuation of the 3 parameters/columns is similar except on several points.
# This plot supports the Correlation among these three columns.
df1[['temperature_2m','dew_point_2m']].plot(subplots=[('temperature_2m','dew_point_2m')])

array([<Axes: >], dtype=object)

# Draw plot of the most correlated column with 'temperature_2m'
# The 'temperature_2m' and 'vapour_pressure_deficit' fluctuations for the most part are similar except at several points.
df04 = df1.where(df1['month'] == '04').dropna()
df05 = df1.where(df1['month'] == '05').dropna()

import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('temperature_2m vs vapour_pressure_deficit in April 2025')
ax1.plot(df04[['temperature_2m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['vapour_pressure_deficit']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('temperature_2m vs vapour_pressure_deficit in May 2025')
ax1.plot(df05[['temperature_2m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['vapour_pressure_deficit']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x17f9d0b0c50>]

# Generate Correlation Table for 'relative_humidity_2m'
gencorr('relative_humidity_2m')

relative_humidity_2m       1.00
vapour_pressure_deficit    0.86
soil_moisture_0_to_1cm     0.59
soil_moisture_1_to_3cm     0.57
dew_point_2m               0.56
soil_moisture_3_to_9cm     0.53
cloud_cover_low            0.52
Name: relative_humidity_2m, dtype: float64

# Draw plot of the most correlated column with 'relative_humidity_2m'
# The relative_humidity_2m values are the good reverse of the vapour_pressure_deficit for the most part.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('relative_humidity_2m vs vapour_pressure_deficit in April 2025')
ax1.plot(df04[['relative_humidity_2m']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['vapour_pressure_deficit']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('relative_humidity_2m vs vapour_pressure_deficit in May 2025')
ax1.plot(df05[['relative_humidity_2m']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['vapour_pressure_deficit']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x17f9cfea180>]

# Generate Correlation Table for 'dew_point_2m'
gencorr('dew_point_2m')

dew_point_2m            1.00
temperature_80m         0.71
soil_temperature_0cm    0.63
temperature_2m          0.63
relative_humidity_2m    0.56
Name: dew_point_2m, dtype: float64

# Draw plot of the most correlated column with 'dew_point_2m'
# The 'dew_point_2m' and 'temperature_2m' fluctuations for more than half part are similar except at several points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['dew_point_2m','temperature_2m']].plot(title='dew_point_2m vs temperature_2m in April 2025')
df05[['dew_point_2m','temperature_2m']].plot(title='dew_point_2m vs temperature_2m in May 2025')

<Axes: title={'center': 'dew_point_2m vs temperature_2m in May 2025'}>

# Generate Correlation Table for 'precipitation_probability'
gencorr('precipitation_probability')

precipitation_probability    1.00
cloud_cover_mid              0.51
Name: precipitation_probability, dtype: float64

# Draw plot of the most correlated column with 'precipitation_probability'
# The 'precipitation_probability' and 'cloud_cover_mid' fluctuations for the half part are correlated.
import matplotlib.pyplot as plt
df04[['precipitation_probability','cloud_cover_mid']].plot(title='precipitation_probability vs cloud_cover_mid in April 2025')
df05[['precipitation_probability','cloud_cover_mid']].plot(title='precipitation_probability vs cloud_cover_mid in May 2025')

<Axes: title={'center': 'precipitation_probability vs cloud_cover_mid in May 2025'}>

# Generate Correlation Table for 'cloud_cover_low'
gencorr('cloud_cover_low')

cloud_cover_low         1.00
relative_humidity_2m    0.52
Name: cloud_cover_low, dtype: float64

# Draw plot of the most correlated column with 'cloud_cover_low'
# The 'cloud_cover_low' and 'cloud_cover' fluctuations for more than half part are similar except at several points.
import matplotlib.pyplot as plt
df04[['cloud_cover_low','cloud_cover']].plot(title='cloud_cover_low vs cloud_cover in April 2025')
df05[['cloud_cover_low','cloud_cover']].plot(title='cloud_cover_low vs cloud_cover in May 2025')

<Axes: title={'center': 'cloud_cover_low vs cloud_cover in May 2025'}>

# Generate Correlation Table for 'cloud_cover_mid'
gencorr('cloud_cover_mid')

cloud_cover_mid              1.00
cloud_cover_high             0.57
precipitation_probability    0.51
Name: cloud_cover_mid, dtype: float64

# Draw plot of the most correlated column with 'cloud_cover_mid'
# The 'cloud_cover_mid' and 'cloud_cover' fluctuations for more than half part are not so similar.
import matplotlib.pyplot as plt
df04[['cloud_cover_mid','cloud_cover']].plot(title='cloud_cover_mid vs cloud_cover in April 2025')
df05[['cloud_cover_mid','cloud_cover']].plot(title='cloud_cover_mid vs cloud_cover in May 2025')

<Axes: title={'center': 'cloud_cover_mid vs cloud_cover in May 2025'}>

# Generate Correlation Table for 'cloud_cover_high'
gencorr('cloud_cover_high')

cloud_cover_high    1.00
cloud_cover_mid     0.57
Name: cloud_cover_high, dtype: float64

# Draw plot of the most correlated column with 'cloud_cover_high'
# The 'cloud_cover_high' and 'cloud_cover_mid' fluctuations for more than half part are not so similar.
import matplotlib.pyplot as plt
df04[['cloud_cover_high','cloud_cover_mid']].plot(title='cloud_cover_high vs cloud_cover_mid in April 2025')
df05[['cloud_cover_high','cloud_cover_mid']].plot(title='cloud_cover_high vs cloud_cover_mid in May 2025')

<Axes: title={'center': 'cloud_cover_high vs cloud_cover_mid in May 2025'}>

# Generate Correlation Table for 'wind_speed_10m'
gencorr('wind_speed_10m')

wind_speed_10m    1.0
Name: wind_speed_10m, dtype: float64

# Draw plot of the most correlated column with 'wind_speed_10m'
# The 'wind_speed_10m' and 'wind_gusts_10m' fluctuations for the most part are similar except at several points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['wind_speed_10m','wind_gusts_10m']].plot(title='wind_speed_10m vs wind_gusts_10m in April 2025')
df05[['wind_speed_10m','wind_gusts_10m']].plot(title='wind_speed_10m vs wind_gusts_10m in May 2025')

<Axes: title={'center': 'wind_speed_10m vs wind_gusts_10m in May 2025'}>

# Generate Correlation Table for 'soil_moisture_0_to_1cm'
gencorr('soil_moisture_0_to_1cm')

soil_moisture_0_to_1cm     1.00
soil_moisture_1_to_3cm     0.99
soil_moisture_3_to_9cm     0.93
relative_humidity_2m       0.59
vapour_pressure_deficit    0.55
Name: soil_moisture_0_to_1cm, dtype: float64

# Draw plot of the most correlated column with 'soil_moisture_0_to_1cm'
# The 'soil_moisture_0_to_1cm' and 'soil_moisture_1_to_3cm' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_moisture_0_to_1cm','soil_moisture_1_to_3cm']].plot(title='soil_moisture_0_to_1cm vs soil_moisture_1_to_3cm in April 2025')
df05[['soil_moisture_0_to_1cm','soil_moisture_1_to_3cm']].plot(title='soil_moisture_0_to_1cm vs soil_moisture_1_to_3cm in May 2025')

<Axes: title={'center': 'soil_moisture_0_to_1cm vs soil_moisture_1_to_3cm in May 2025'}>

# Generate Correlation Table for 'temperature_80m'
gencorr('temperature_80m')

temperature_80m            1.00
temperature_2m             0.96
soil_temperature_0cm       0.86
dew_point_2m               0.71
vapour_pressure_deficit    0.52
Name: temperature_80m, dtype: float64

# Draw plot of the most correlated column with 'temperature_80m'
# The 'temperature_80m' and 'temperature_2m' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['temperature_80m','temperature_2m']].plot(title='temperature_80m vs temperature_2m in April 2025')
df05[['temperature_80m','temperature_2m']].plot(title='temperature_80m vs temperature_2m in May 2025')

<Axes: title={'center': 'temperature_80m vs temperature_2m in May 2025'}>

# Generate Correlation Table for 'soil_temperature_0cm'
gencorr('soil_temperature_0cm')

soil_temperature_0cm       1.00
temperature_2m             0.92
temperature_80m            0.86
dew_point_2m               0.63
vapour_pressure_deficit    0.54
Name: soil_temperature_0cm, dtype: float64

# Draw plot of the most correlated column with 'soil_temperature_0cm'
# The 'soil_temperature_0cm' and 'temperature_2m' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_temperature_0cm','temperature_2m']].plot(title='soil_temperature_0cm vs temperature_2m in April 2025')
df05[['soil_temperature_0cm','temperature_2m']].plot(title='soil_temperature_0cm vs temperature_2m in May 2025')

<Axes: title={'center': 'soil_temperature_0cm vs temperature_2m in May 2025'}>

# Generate Correlation Table for 'soil_moisture_1_to_3cm'
gencorr('soil_moisture_1_to_3cm')

soil_moisture_1_to_3cm     1.00
soil_moisture_0_to_1cm     0.99
soil_moisture_3_to_9cm     0.96
relative_humidity_2m       0.57
vapour_pressure_deficit    0.52
Name: soil_moisture_1_to_3cm, dtype: float64

# Draw plot of the most correlated column with 'soil_moisture_1_to_3cm'
# The 'soil_moisture_1_to_3cm' and 'soil_moisture_0_to_1cm' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_moisture_1_to_3cm','soil_moisture_0_to_1cm']].plot(title='soil_moisture_1_to_3cm vs soil_moisture_0_to_1cm in April 2025')
df05[['soil_moisture_1_to_3cm','soil_moisture_0_to_1cm']].plot(title='soil_moisture_1_to_3cm vs soil_moisture_0_to_1cm in May 2025')

<Axes: title={'center': 'soil_moisture_1_to_3cm vs soil_moisture_0_to_1cm in May 2025'}>

# Generate Correlation Table for 'soil_moisture_3_to_9cm'
gencorr('soil_moisture_3_to_9cm')

soil_moisture_3_to_9cm    1.00
soil_moisture_1_to_3cm    0.96
soil_moisture_0_to_1cm    0.93
relative_humidity_2m      0.53
Name: soil_moisture_3_to_9cm, dtype: float64

# Draw plot of the most correlated column with 'soil_moisture_3_to_9cm'
# The 'soil_moisture_3_to_9cm' and 'soil_moisture_1_to_3cm' fluctuations for the most part are similar except at very few points.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['soil_moisture_3_to_9cm','soil_moisture_1_to_3cm']].plot(title='soil_moisture_3_to_9cm vs soil_moisture_1_to_3cm in April 2025')
df05[['soil_moisture_3_to_9cm','soil_moisture_1_to_3cm']].plot(title='soil_moisture_3_to_9cm vs soil_moisture_1_to_3cm in May 2025')

<Axes: title={'center': 'soil_moisture_3_to_9cm vs soil_moisture_1_to_3cm in May 2025'}>

# Generate Correlation Table for 'vapour_pressure_deficit'
gencorr('vapour_pressure_deficit')

vapour_pressure_deficit    1.00
relative_humidity_2m       0.86
temperature_2m             0.65
soil_moisture_0_to_1cm     0.55
soil_temperature_0cm       0.54
temperature_80m            0.52
soil_moisture_1_to_3cm     0.52
Name: vapour_pressure_deficit, dtype: float64

# Draw plot of the most correlated column with 'vapour_pressure_deficit'
# The 'vapour_pressure_deficit' and 'soil_moisture_0_to_1cm' fluctuations for more than half part are similar.
import matplotlib.pyplot as plt
# plt.title('dew_point_2m vs temperature_2m in April 2025')
df04[['vapour_pressure_deficit','soil_moisture_0_to_1cm']].plot(title='vapour_pressure_deficit vs soil_moisture_0_to_1cm in April 2025')
df05[['vapour_pressure_deficit','soil_moisture_0_to_1cm']].plot(title='vapour_pressure_deficit vs soil_moisture_0_to_1cm in May 2025')

<Axes: title={'center': 'vapour_pressure_deficit vs soil_moisture_0_to_1cm in May 2025'}>

# Draw plot of the most correlated column with 'vapour_pressure_deficit'
# The vapour_pressure_deficit values are the good reverse of the vapour_pressure_deficit for the most part.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
plt.title('vapour_pressure_deficit vs soil_temperature_0cm in April 2025')
ax1.plot(df04[['vapour_pressure_deficit']], color='blue')
ax2 = ax1.twinx()
ax2.plot(df04[['soil_temperature_0cm']], color='green', label='Data 2')

fig, ax1 = plt.subplots()
plt.title('vapour_pressure_deficit vs soil_temperature_0cm in May 2025')
ax1.plot(df05[['vapour_pressure_deficit']], color='blue', label='Data 1')
ax2 = ax1.twinx()
ax2.plot(df05[['soil_temperature_0cm']], color='green', label='Data 2')

[<matplotlib.lines.Line2D at 0x17f9ce3ecc0>]

# Draw the Heatmap of Weather Data Correlation
import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(df1h.corr(numeric_only=True), annot=True)
plt.rcParams['figure.figsize'] = (20,10)
plt.show()

# How about if we limit the 'temperature_2m' to certain range, let's say, more than 18C
# Data for 4 Columns where temperature_2m more than 18 C
df1h2 = df1h.where(df1h['temperature_2m'] > 18)
df1h2 = df1h2.dropna()
df1h2

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
595	0.0	100.0	0.231	0.238	72.0	13.068562	0.585521	18.1995	100.0	100.0	26.072052	0.253	17.660500	23.367500
596	1.0	100.0	0.229	0.237	69.0	13.279072	0.685738	19.0995	100.0	100.0	26.987997	0.252	18.660500	21.267500
597	1.0	0.0	0.229	0.237	70.0	13.691494	0.671908	19.2995	5.0	100.0	28.241076	0.252	18.910500	21.917501
598	2.0	0.0	0.228	0.236	69.0	14.139482	0.725077	19.9995	0.0	97.0	28.467329	0.251	19.060501	21.867500
599	8.0	0.0	0.227	0.235	71.0	14.197729	0.661702	19.5995	0.0	56.0	28.241076	0.251	19.110500	21.217500
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	3.0	33.0	0.265	0.269	76.0	17.093630	0.615556	21.5025	90.0	100.0	5.091168	0.275	21.160500	25.567501
2228	2.0	100.0	0.262	0.266	77.0	17.687681	0.604456	21.9025	100.0	98.0	5.860375	0.273	20.560501	25.567501
2229	3.0	100.0	0.261	0.264	76.0	17.577087	0.634595	22.0025	100.0	100.0	9.387651	0.271	20.210500	24.367500
2230	6.0	100.0	0.260	0.263	75.0	16.932825	0.643173	21.5525	100.0	74.0	12.599998	0.270	19.360500	24.967500
2231	10.0	100.0	0.259	0.262	75.0	16.208752	0.614321	20.8025	100.0	71.0	12.287555	0.268	17.710500	22.217500

884 rows × 14 columns

sns.heatmap(df1h2.corr(numeric_only=True), annot=True)
plt.rcParams['figure.figsize'] = (20,10)
plt.show()

######### Conclusion from the Heatmap ##############
# 'relative_humidity_2m' has correlation of -0.86 to 'vapour_pressure_deficit'.
# 'temperature_80m' has correlation of 0.92 to 'temperature_2m'.
# 'temperature_80m' has correlation of 0.86 to 'soil_temperature_0cm'.
# 'temperature_80m' has correlation of 0.71 to 'dew_point_2m'.
# 'temperature_2m' has correlation of 0.92 to 'soil_temperature_0cm'.
# 'dew_point_2m' has correlation of 0.81 to 'relative_humidity_2m' parameter if temperature_2m > 18C, compared to 0.56 with all data included.
# 'cloud_cover_mid' has correlation of 0.51 to 'precipitation_probability'.
# 'cloud_cover_low' has correlation of 0.52 to 'relative_humidity_2m'
# 'soil_moisture_0_to_1cm' has correlation of 0.59 to 'relative_humidity_2m'.
# 'soil_temperature_0cm has correlation of 0.63 to 'dew_point_2m'.
# 'vapour_pressure_deficit' has correlation of 0.65 to 'temperature_2m'.

# LAB : Check 'corr' method of Pandas with mathematical high order polynomial equations (quadratic to power-of-5 equations)
# Equations used:
# 1. y1 = 2x^2 + 5x + 6
# 2. y2 = 2x^3 + 7x^2 + 10x + 20
# 3. y3 = 5x^4 + 12x^3 + 8x^2 + 6x + 20
# 4. y4 = 3x^5 + 8x^4 + 10x^3 + 4x^2 + 9x + 17
# 5. y5 = 2x^7 + 9x^6 + 12x^5 + 31x^4 + 90x^3 + 6x^2 + 78x + 111
dfq = pd.read_csv(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\Global_Weather\quadratic_equation.csv')
dfq

	x	y1	y2	y3	y4	y5
0	1	13	39	51	51	339
1	2	24	84	240	355	2723
2	3	39	167	839	1727	19191
3	4	58	300	2220	5877	96135
4	5	81	495	4875	15787	365651
5	6	108	764	9416	36071	1133499
6	7	139	1119	16575	73335	3013863
7	8	174	1572	27204	136537	7120991
8	9	213	2135	42275	237347	15327795
9	10	256	2820	62880	390507	30601491
10	11	303	3639	90231	614191	57426359
11	12	354	4604	125660	930365	102323703
12	13	409	5727	170619	1365147	174479091
13	14	468	7020	226680	1949167	286486955
14	15	531	8495	295535	2717927	455222631
15	16	598	10164	378996	3712161	702851919
16	17	669	12039	478995	4978195	1057988243
17	18	744	14132	597584	6568307	1557007491

sns.heatmap(dfq.corr(numeric_only=True), annot=True)
plt.rcParams['figure.figsize'] = (20,10)
plt.show()

# CONCLUSION: 
# 'corr' Method of Pandas is accurate enough until cube equation, but for higher order equation, is much less.
# Correlation value of 0.8 is still can be regarded as correlated enough, due to corr method algorithm limitation.

# Trendline of Polynomial Equations of Dataset x and y1 from the previous Lab Dataset.
# Convert the data in Dataframe into a basic List
dfqdictx = dfq[['x']].to_dict(orient='records')
dfqdicty1 = dfq[['y1']].to_dict(orient='records')
x = []
y1 = []

for i in dfqdictx:
    x.append(i['x'])

for i in dfqdicty1:
    y1.append(i['y1'])
    
import matplotlib.pyplot as plt
import numpy as np

#create scatterplot
plt.scatter(x, y1)

#calculate equation for quadratic trendline
z = np.polyfit(x, y1, 2)
p = np.poly1d(z)
print(p)

#add trendline to plot
plt.plot(x, p(x))

   2
2 x + 5 x + 6

[<matplotlib.lines.Line2D at 0x17fa354f710>]

# Conclusion: it's proven that the Trendline equation made by 'poly1d' Function is exactly the same as the original one.

# Trendline of Polynomial Equations of Dataset x and y2 from the previous Lab Dataset.
# Convert the data in Dataframe into a basic List
dfqdicty2 = dfq[['y2']].to_dict(orient='records')
y2 = []

for i in dfqdicty2:
    y2.append(i['y2'])
    
import matplotlib.pyplot as plt

#create scatterplot
plt.scatter(x, y2)

#calculate equation for quadratic trendline
z = np.polyfit(x, y2, 3)
p = np.poly1d(z)  # It's proven that poly1d is successfully create the polynomial equation for y2 dataset
print(p)

#add trendline to plot
plt.plot(x, p(x))

   3     2
2 x + 7 x + 10 x + 20

[<matplotlib.lines.Line2D at 0x17fa5ba7860>]

# Trendline of Polynomial Equations of Dataset x and y3 from the previous Lab Dataset.
# Convert the data in Dataframe into a basic List
dfqdicty3 = dfq[['y3']].to_dict(orient='records')
y3 = []

for i in dfqdicty3:
    y3.append(i['y3'])
    
import matplotlib.pyplot as plt

#create scatterplot
plt.scatter(x, y3)

#calculate equation for quadratic trendline
z = np.polyfit(x, y3, 4)
p = np.poly1d(z)  # It's proven that poly1d is successfully create the polynomial equation for y3 dataset
print(p)

#add trendline to plot
plt.plot(x, p(x))

   4      3     2
5 x + 12 x + 8 x + 6 x + 20

[<matplotlib.lines.Line2D at 0x17fa4d3be90>]

# Trendline of Polynomial Equations of Dataset x and y4 from the previous Lab Dataset.
# Convert the data in Dataframe into a basic List
dfqdicty4 = dfq[['y4']].to_dict(orient='records')
y4 = []

for i in dfqdicty4:
    y4.append(i['y4'])
    
import matplotlib.pyplot as plt

#create scatterplot
plt.scatter(x, y4)

#calculate equation for quadratic trendline
z = np.polyfit(x, y4, 5)
p = np.poly1d(z)  # It's proven that poly1d is successfully create the polynomial equation for y4 dataset
print(p)

#add trendline to plot
plt.plot(x, p(x))

   5     4      3     2
3 x + 8 x + 10 x + 4 x + 9 x + 17

[<matplotlib.lines.Line2D at 0x17fa4deb890>]

# Trendline of Polynomial Equations of Dataset x and y5 from the previous Lab Dataset.
# Convert the data in Dataframe into a basic List
dfqdicty5 = dfq[['y5']].to_dict(orient='records')
y5 = []

for i in dfqdicty5:
    y5.append(i['y5'])
    
import matplotlib.pyplot as plt

#create scatterplot
plt.scatter(x, y5)

#calculate equation for quadratic trendline
z = np.polyfit(x, y5, 7)
p = np.poly1d(z)  # It's proven that poly1d is successfully create the polynomial equation for y5 dataset
print(p)

#add trendline to plot
plt.plot(x, p(x))

   7     6      5      4      3     2
2 x + 9 x + 12 x + 31 x + 90 x + 6 x + 78 x + 111

[<matplotlib.lines.Line2D at 0x17fa5395220>]

# The plot of all data of all Columns/parameters
df1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
581	581	2025-03-19 05:00:00+00:00	16.7995	53.0	7.163813	13.256210	0.0	0.0	0.0	0.0	1012.3	993.66210	100.0	0.0	0.0	100.0	24140.0	19.296133	0.233	0.240	0.257	0.289	0.312	0.0	0.0	0.0	0.097479	0.900061	38.034130	41.253933	57.802147	194.036270	197.062940	197.628110	193.690060	17.460500	17.3605	16.867500	13.617499	14.517500	14.317500	12.117499	58.320000	Nashville	2025-03-19	05:00:00+00:00	2025	03	19	05	00	00+00	00
582	582	2025-03-19 06:00:00+00:00	16.6995	53.0	7.071089	13.207399	0.0	0.0	0.0	0.0	1012.2	993.55770	100.0	0.0	0.0	100.0	24140.0	18.792551	0.232	0.240	0.257	0.288	0.312	0.0	0.0	0.0	0.095071	0.894375	37.123665	39.995990	54.588394	196.699330	199.840790	200.432900	192.569780	17.510500	17.4105	17.367500	14.917500	15.267500	14.517500	12.117499	57.600000	Nashville	2025-03-19	06:00:00+00:00	2025	03	19	06	00	00+00	00
583	583	2025-03-19 07:00:00+00:00	15.8495	57.0	7.343195	12.507574	0.0	0.0	0.0	0.0	1012.3	993.60156	100.0	0.0	0.0	100.0	24140.0	18.214718	0.232	0.240	0.256	0.288	0.311	0.0	0.0	0.0	0.080870	0.775166	35.900864	39.098087	54.206000	198.435040	201.161330	201.501500	196.990900	16.460500	16.3605	16.367500	14.517500	14.867499	14.467500	12.167500	58.320000	Nashville	2025-03-19	07:00:00+00:00	2025	03	19	07	00	00+00	00
584	584	2025-03-19 08:00:00+00:00	15.3495	59.0	7.379331	12.011299	0.0	0.0	0.0	0.0	1011.8	993.07860	100.0	0.0	0.0	100.0	24140.0	18.250260	0.232	0.240	0.256	0.287	0.311	0.0	0.0	0.0	0.075079	0.715844	35.747110	38.583614	51.580600	194.858660	198.799990	199.470400	199.573210	15.910501	15.8105	15.367499	14.417500	14.667500	14.417500	12.167500	57.960000	Nashville	2025-03-19	08:00:00+00:00	2025	03	19	08	00	00+00	00
585	585	2025-03-19 09:00:00+00:00	14.6495	61.0	7.208212	11.212486	0.0	0.0	0.0	0.0	1011.4	992.64110	100.0	0.0	0.0	100.0	24140.0	18.643690	0.232	0.240	0.256	0.287	0.311	0.0	0.0	0.0	0.070025	0.650970	35.188046	38.145466	48.876540	190.007920	193.609760	193.366940	200.704300	15.160501	15.0605	14.567500	13.567500	14.367499	14.367499	12.217500	58.320000	Nashville	2025-03-19	09:00:00+00:00	2025	03	19	09	00	00+00	00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2227	2227	2025-05-26 19:00:00+00:00	21.5025	76.0	17.093630	23.275127	3.0	0.0	0.0	0.0	1018.7	1000.24040	100.0	100.0	33.0	90.0	21100.0	5.091168	0.265	0.269	0.275	0.274	0.296	0.0	0.0	0.0	0.311080	0.615556	6.519877	19.110743	19.642280	81.869990	83.659904	88.898320	63.904633	21.160500	20.9105	19.767500	25.567501	23.167501	19.467500	19.367500	9.360000	Nashville	2025-05-26	19:00:00+00:00	2025	05	26	19	00	00+00	00
2228	2228	2025-05-26 20:00:00+00:00	21.9025	77.0	17.687681	23.821278	2.0	0.0	0.0	0.0	1018.1	999.67584	100.0	98.0	100.0	100.0	20700.0	5.860375	0.262	0.266	0.273	0.274	0.296	0.0	0.0	0.0	0.215838	0.604456	6.877789	20.329449	20.326454	42.510403	47.121110	83.774260	67.067870	20.560501	20.3605	20.667501	25.567501	23.267500	19.817501	19.367500	8.640000	Nashville	2025-05-26	20:00:00+00:00	2025	05	26	20	00	00+00	00
2229	2229	2025-05-26 21:00:00+00:00	22.0025	76.0	17.577087	23.358010	3.0	0.0	0.0	0.0	1017.5	999.09290	100.0	100.0	100.0	100.0	21200.0	9.387651	0.261	0.264	0.271	0.273	0.295	0.0	0.0	0.0	0.242568	0.634595	12.181624	22.123201	20.140705	32.471172	34.159744	85.236440	65.725570	20.210500	20.0105	20.867500	24.367500	23.017500	20.117500	19.367500	12.599999	Nashville	2025-05-26	21:00:00+00:00	2025	05	26	21	00	00+00	00
2230	2230	2025-05-26 22:00:00+00:00	21.5525	75.0	16.932825	22.161495	6.0	0.0	0.0	0.0	1017.1	998.67255	100.0	74.0	100.0	100.0	21300.0	12.599998	0.260	0.263	0.270	0.273	0.295	0.0	0.0	0.0	0.169983	0.643173	18.792550	16.370962	17.782688	53.130020	53.569054	99.039406	68.629310	19.360500	19.1605	21.167501	24.967500	22.967500	20.367500	19.367500	19.800000	Nashville	2025-05-26	22:00:00+00:00	2025	05	26	22	00	00+00	00
2231	2231	2025-05-26 23:00:00+00:00	20.8025	75.0	16.208752	21.156380	10.0	0.0	0.0	0.0	1017.1	998.62580	100.0	71.0	100.0	100.0	21100.0	12.287555	0.259	0.262	0.268	0.273	0.295	0.0	0.0	0.0	0.159207	0.614321	19.513195	19.695263	18.331741	58.172600	60.124096	81.416466	70.497470	17.710500	17.6105	20.967500	22.217500	22.667501	20.567501	19.367500	21.240000	Nashville	2025-05-26	23:00:00+00:00	2025	05	26	23	00	00+00	00

1651 rows × 53 columns

# Try to find the other Columns value when the 'temperature_2m' is maximum
#temperature_2m_max = df[df1.columns[1]].max()  # OR temperature_2m_max = df['temperature_2m'].max()
temperature_2m_max = df1['temperature_2m'].max()
print(temperature_2m_max)
df1ht2mmax = df1.where(df1['temperature_2m'] == temperature_2m_max)
df1ht2mmax = df1ht2mmax.dropna()
df1ht2mmax[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

30.8525

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
2013	2025-05-17 21:00:00+00:00	30.8525	28.0	10.237361	0.0	0.0	0.0	0.0	26.525429	0.284	0.291	0.302	3.207373	27.4605	31.5175

# Try to find the other Columns value when the 'temperature_2m' is minimum
temperature_2m_min = df1['temperature_2m'].min()
print(temperature_2m_min)
df1ht2mmin = df1.where(df1['temperature_2m'] == temperature_2m_min)
df1ht2mmin = df1ht2mmin.dropna()
df1ht2mmin[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

-2.7975001

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
636	2025-03-21 12:00:00+00:00	-2.7975	87.0	-4.659359	0.0	10.0	0.0	0.0	1.018234	0.267	0.269	0.273	0.06488	2.7105	-3.0825

# Find values when the 'precipitation_probability' is Maximum
precipitation_probability_max = df1['precipitation_probability'].max()
print(precipitation_probability_max)
df1hppmax = df1.where(df1['precipitation_probability'] == precipitation_probability_max).sort_values(by = ['cloud_cover_mid','soil_moisture_0_to_1cm'], ascending=[False,False])
df1hppmax = df1hppmax.dropna()
df1hppmax[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm',
           'cloud_cover_low','cloud_cover_high','wind_speed_10m','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

100.0

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	cloud_cover_low	cloud_cover_high	wind_speed_10m	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
1014	2025-04-06 06:00:00+00:00	14.902500	97.0	14.430263	100.0	100.0	0.417	100.0	100.0	12.229406	0.416	0.412	0.050880	15.310500	16.667501
1015	2025-04-06 07:00:00+00:00	15.752501	99.0	15.595463	100.0	100.0	0.406	99.0	100.0	7.968939	0.404	0.398	0.017908	15.510500	16.617500
1013	2025-04-06 05:00:00+00:00	15.302500	96.0	14.668031	100.0	100.0	0.396	100.0	100.0	9.957109	0.393	0.393	0.069604	14.760500	18.067501
1017	2025-04-06 09:00:00+00:00	16.502500	96.0	15.862131	100.0	100.0	0.390	99.0	100.0	9.793058	0.395	0.406	0.075138	15.210501	14.667500

# Find values when the 'precipitation_probability' is 50%
df1hpp50 = df1.where(df1['precipitation_probability'] == 50).sort_values(by = ['cloud_cover_mid'], ascending=[False])
df1hpp50 = df1hpp50.dropna()
df1hpp50[['date','temperature_2m','relative_humidity_2m','dew_point_2m','precipitation_probability','cloud_cover_mid','cloud_cover_low',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']]

	date	temperature_2m	relative_humidity_2m	dew_point_2m	precipitation_probability	cloud_cover_mid	cloud_cover_low	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
2055	2025-05-19 15:00:00+00:00	20.0025	96.0	19.344767	50.0	99.0	100.0	99.0	6.379216	0.262	0.263	0.261	0.093540	20.160500	27.417501
699	2025-03-24 03:00:00+00:00	17.2525	90.0	15.596647	50.0	98.0	99.0	4.0	16.595179	0.381	0.381	0.393	0.196987	15.910501	15.917500
1897	2025-05-13 01:00:00+00:00	19.1525	91.0	17.647644	50.0	95.0	7.0	99.0	2.741678	0.315	0.317	0.322	0.199684	20.010500	19.617500
1228	2025-04-15 04:00:00+00:00	17.7025	64.0	10.806604	50.0	61.0	100.0	0.0	15.188417	0.358	0.307	0.226	0.729761	17.610500	17.017500

# Find values when the 'precipitation_probability' is Minimum
precipitation_probability_min = df1['precipitation_probability'].min()
print(precipitation_probability_min)
df1hppmin = df1.where(df1['precipitation_probability'] == precipitation_probability_min).sort_values(by = ['cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[True,True,True,True,True,True,True,True]).head(5)
df1hppmin = df1hppmin.dropna()
df1hppmin[['date','temperature_2m','precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm',
           'soil_moisture_3_to_9cm','relative_humidity_2m','cloud_cover_high','dew_point_2m','vapour_pressure_deficit',
           'cloud_cover_low','wind_speed_10m','soil_moisture_1_to_3cm','temperature_80m','soil_temperature_0cm']]

0.0

	date	temperature_2m	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_3_to_9cm	relative_humidity_2m	cloud_cover_high	dew_point_2m	vapour_pressure_deficit	cloud_cover_low	wind_speed_10m	soil_moisture_1_to_3cm	temperature_80m	soil_temperature_0cm
1374	2025-04-21 06:00:00+00:00	22.3525	0.0	0.0	0.149	0.195	58.0	9.0	13.688183	1.134731	0.0	17.771753	0.168	21.3605	20.267500
1373	2025-04-21 05:00:00+00:00	22.5025	0.0	0.0	0.149	0.196	53.0	0.0	12.447384	1.281522	0.0	13.441071	0.168	21.4605	19.817501
1341	2025-04-19 21:00:00+00:00	27.7025	0.0	0.0	0.161	0.202	45.0	98.0	14.694766	2.042824	5.0	10.799999	0.179	25.6105	31.167501
1340	2025-04-19 20:00:00+00:00	28.1025	0.0	0.0	0.163	0.203	46.0	63.0	15.399475	2.052846	27.0	12.522619	0.180	25.6605	32.467500
1322	2025-04-19 02:00:00+00:00	25.0025	0.0	0.0	0.169	0.210	36.0	0.0	8.896405	2.028199	0.0	7.628263	0.186	22.6605	20.667501

# Try to find the other Columns value when the 'relative_humidity_2m' is maximum
relative_humidity_2m_max = df1['relative_humidity_2m'].max()
print(relative_humidity_2m_max)
df1hrh2mmax = df1.where(df1['relative_humidity_2m'] == relative_humidity_2m_max)
df1hrh2mmax = df1hrh2mmax.dropna()
df1hrh2mmax[['date','relative_humidity_2m','temperature_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']].sort_values(by='precipitation_probability', ascending=False)

100.0

	date	relative_humidity_2m	temperature_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
1016	2025-04-06 08:00:00+00:00	100.0	15.8025	15.802500	99.0	61.0	98.0	100.0	15.696165	0.427	0.432	0.440	0.000000e+00	15.810500	15.867499
1010	2025-04-06 02:00:00+00:00	100.0	16.1025	16.102500	87.0	100.0	100.0	98.0	3.396233	0.400	0.402	0.361	0.000000e+00	13.610500	20.617500
951	2025-04-03 15:00:00+00:00	100.0	18.9525	18.952500	86.0	100.0	76.0	0.0	2.305125	0.377	0.376	0.377	0.000000e+00	17.460500	21.717500
1009	2025-04-06 01:00:00+00:00	100.0	17.1525	17.152498	72.0	100.0	100.0	98.0	6.479999	0.332	0.306	0.294	1.192093e-07	12.910501	21.717500
952	2025-04-03 16:00:00+00:00	100.0	19.5025	19.502502	57.0	100.0	95.0	0.0	5.815978	0.339	0.350	0.362	0.000000e+00	18.010500	23.867500
2080	2025-05-20 16:00:00+00:00	100.0	22.1025	22.102500	45.0	100.0	99.0	99.0	8.404285	0.295	0.295	0.288	0.000000e+00	23.660500	24.317501
1671	2025-05-03 15:00:00+00:00	100.0	16.3525	16.352500	37.0	100.0	100.0	97.0	13.217443	0.347	0.350	0.356	0.000000e+00	16.510500	17.417501
1457	2025-04-24 17:00:00+00:00	100.0	19.5025	19.502502	35.0	100.0	100.0	58.0	1.484318	0.306	0.308	0.305	0.000000e+00	17.560501	28.117500
1741	2025-05-06 13:00:00+00:00	100.0	11.9525	11.952499	0.0	100.0	0.0	0.0	3.096837	0.297	0.299	0.303	1.192093e-07	11.710501	14.967500
1790	2025-05-08 14:00:00+00:00	100.0	18.6525	18.652498	0.0	100.0	0.0	0.0	0.509117	0.306	0.308	0.311	4.768372e-07	18.010500	22.417501
1932	2025-05-14 12:00:00+00:00	100.0	16.7525	16.752500	0.0	100.0	0.0	0.0	5.804825	0.300	0.302	0.307	0.000000e+00	16.510500	18.467500
1934	2025-05-14 14:00:00+00:00	100.0	19.1525	19.152498	0.0	100.0	0.0	0.0	8.404285	0.297	0.300	0.306	2.384186e-07	19.010500	23.767500

# Conclusion: when 'relative_humidity_2m' reaches maximum, the 'precipitation_probability' is one of the highest also.
# This concurs to common logic, in contrary to condition in Las Vegas
# (precipitation_probability is extremely low when the 'relative_humidity_2m' reaches maximum).

# Find values when the 'relative_humidity_2m' is 50%
df1hrh50 = df1.where(df1['relative_humidity_2m'] == 50).sort_values(by = ['precipitation_probability'], ascending=[False])
df1hrh50 = df1hrh50.dropna()
df1hrh50[['date','relative_humidity_2m','temperature_2m','vapour_pressure_deficit','dew_point_2m','precipitation_probability','cloud_cover_mid','cloud_cover_low',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','temperature_80m','soil_temperature_0cm']]

	date	relative_humidity_2m	temperature_2m	vapour_pressure_deficit	dew_point_2m	precipitation_probability	cloud_cover_mid	cloud_cover_low	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
1227	2025-04-15 03:00:00+00:00	50.0	23.252500	1.426596	12.251946	39.0	0.0	97.0	0.0	2.305125	0.307	0.210	0.216	18.810501	18.417501
1439	2025-04-23 23:00:00+00:00	50.0	26.402500	1.720850	15.145651	19.0	13.0	16.0	99.0	10.137692	0.267	0.249	0.260	23.460500	24.867500
1225	2025-04-15 01:00:00+00:00	50.0	25.702500	1.651252	14.502851	11.0	0.0	10.0	0.0	12.261158	0.177	0.193	0.216	21.960500	22.467500
832	2025-03-29 16:00:00+00:00	50.0	22.152500	1.334735	11.240775	4.0	100.0	0.0	98.0	23.675303	0.187	0.196	0.214	19.960500	23.017500
1943	2025-05-14 23:00:00+00:00	50.0	28.052500	1.895097	16.660276	3.0	42.0	100.0	0.0	17.819090	0.306	0.308	0.308	25.210500	26.417501
1941	2025-05-14 21:00:00+00:00	50.0	28.052500	1.895097	16.660276	2.0	3.0	8.0	0.0	17.057314	0.346	0.295	0.298	25.860500	26.967500
827	2025-03-29 11:00:00+00:00	50.0	20.002500	1.169993	9.263409	1.0	15.0	0.0	76.0	9.255571	0.188	0.197	0.216	17.860500	16.467500
2036	2025-05-18 20:00:00+00:00	50.0	27.102500	1.792990	15.788316	1.0	7.0	8.0	17.0	5.052841	0.271	0.276	0.285	25.260500	27.917501
1216	2025-04-14 16:00:00+00:00	50.0	25.952500	1.675822	14.732438	0.0	2.0	0.0	19.0	17.974781	0.200	0.209	0.227	21.760500	29.017500
1307	2025-04-18 11:00:00+00:00	50.0	18.552500	1.069192	7.929086	0.0	99.0	6.0	0.0	15.379206	0.190	0.200	0.220	16.610500	15.167500
1206	2025-04-14 06:00:00+00:00	50.0	17.652500	1.010522	7.100584	0.0	0.0	0.0	0.0	7.421590	0.201	0.211	0.231	17.760500	15.917500
1412	2025-04-22 20:00:00+00:00	50.0	24.852500	1.570049	13.722118	0.0	0.0	33.0	5.0	5.937272	0.249	0.262	0.277	21.810501	28.117500
1414	2025-04-22 22:00:00+00:00	50.0	24.102500	1.501311	13.033067	0.0	0.0	93.0	95.0	4.452954	0.241	0.254	0.272	21.660500	25.517500
1085	2025-04-09 05:00:00+00:00	50.0	4.002500	0.407752	-5.493462	0.0	0.0	0.0	0.0	3.600000	0.241	0.247	0.264	4.510500	1.817500
1611	2025-05-01 03:00:00+00:00	50.0	24.602500	1.546837	13.492453	0.0	76.0	0.0	64.0	6.130579	0.152	0.173	0.205	24.110500	22.417501
1746	2025-05-06 18:00:00+00:00	50.0	21.352500	1.271212	10.505167	0.0	0.0	5.0	0.0	1.609969	0.283	0.287	0.296	18.860500	26.267500
1057	2025-04-08 01:00:00+00:00	50.0	11.002501	0.657563	0.971700	0.0	0.0	0.0	0.0	5.154415	0.259	0.267	0.284	11.060500	8.367499
914	2025-04-02 02:00:00+00:00	50.0	15.502501	0.881633	5.120452	0.0	0.0	0.0	0.0	4.843305	0.241	0.248	0.262	16.260500	12.117499
2019	2025-05-18 03:00:00+00:00	50.0	22.002500	1.322617	11.102864	0.0	0.0	0.0	0.0	4.334974	0.282	0.285	0.293	22.410500	20.117500
1343	2025-04-19 23:00:00+00:00	50.0	26.852500	1.766929	15.558811	0.0	98.0	0.0	98.0	8.404284	0.159	0.177	0.201	24.460500	26.217500

# Conclusion: when 'relative_humidity_2m' reaches 50%, the highest 'precipitation_probability' is 39%.

# Try to find the other Columns value when the 'relative_humidity_2m' is minimum
relative_humidity_2m_min = df1['relative_humidity_2m'].min()
print(relative_humidity_2m_min)
df1hrh2mmin = df1.where(df1['relative_humidity_2m'] == relative_humidity_2m_min)
df1hrh2mmin = df1hrh2mmin.dropna()
df1hrh2mmin[['date','relative_humidity_2m','temperature_2m','dew_point_2m','precipitation_probability','cloud_cover_low','cloud_cover_mid',
            'cloud_cover_high','wind_speed_10m','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm',
            'soil_moisture_3_to_9cm','vapour_pressure_deficit','temperature_80m','soil_temperature_0cm']].sort_values(by='precipitation_probability', ascending=True)

19.0

	date	relative_humidity_2m	temperature_2m	dew_point_2m	precipitation_probability	cloud_cover_low	cloud_cover_mid	cloud_cover_high	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	vapour_pressure_deficit	temperature_80m	soil_temperature_0cm
1269	2025-04-16 21:00:00+00:00	19.0	21.5525	-3.064591	0.0	0.0	0.0	0.0	6.369050	0.217	0.232	0.246	2.085398	17.6605	24.8675
1270	2025-04-16 22:00:00+00:00	19.0	21.5525	-3.064591	0.0	0.0	0.0	37.0	5.400000	0.215	0.229	0.244	2.085398	17.5105	22.7175
1271	2025-04-16 23:00:00+00:00	19.0	20.8025	-3.681689	0.0	0.0	0.0	24.0	5.315336	0.213	0.227	0.243	1.991730	17.2605	19.6175

# Conclusion: when the 'relative_humidity_2m' reaches minimum (19%), the 'precipitation_probability' reaches minimum also (0%).

# Find Daily Average of Precipitation Probability (Table)
dfda1 = df1.groupby('ymd')[df1h.columns[0:]].mean().sort_values('precipitation_probability', ascending=False).head(5)
dfda1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
ymd
2025-04-06	54.833333	93.708333	0.352958	0.354542	96.041667	13.331632	0.061971	13.954583	74.041667	96.625000	8.261871	0.358708	13.395917	15.734167
2025-04-03	51.375000	81.875000	0.298792	0.300750	82.125000	17.717496	0.574346	21.369167	76.250000	71.541667	14.200839	0.309208	20.304250	22.555000
2025-05-03	31.291667	91.291667	0.335875	0.339000	91.250000	15.129816	0.165439	16.571250	89.041667	63.166667	8.347819	0.340208	15.714667	18.230000
2025-03-31	27.500000	49.291667	0.309292	0.311667	87.791667	13.269716	0.212106	15.308750	62.000000	78.166667	10.745450	0.308917	14.941750	17.171667
2025-05-12	26.791667	58.250000	0.341292	0.339500	83.166667	17.631228	0.468887	20.823333	71.666667	74.041667	8.622397	0.335333	19.356333	21.830000

# Find Daily Average of Precipitation Probability (Plot)
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (20, 10)  # Set global size for all plots
dfda2 = df1.groupby('ymd')[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].mean().sort_values('ymd', ascending=True)
dfda2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='ymd'>, <Axes: xlabel='ymd'>], dtype=object)

# Find Daily Average of Relative Humidity 2m (Table)
dfda3 = df1.groupby('ymd')[df1h.columns[[4,0]]].mean().sort_values('relative_humidity_2m', ascending=False).head(10)
dfda3

	relative_humidity_2m	precipitation_probability
ymd
2025-04-06	96.041667	54.833333
2025-05-03	91.250000	31.291667
2025-04-24	90.541667	21.375000
2025-05-08	89.083333	11.708333
2025-04-25	88.916667	24.041667
2025-05-05	88.416667	6.333333
2025-05-04	88.041667	9.750000
2025-03-31	87.791667	27.500000
2025-05-20	87.583333	19.458333
2025-05-09	87.458333	3.166667

# Find Monthly Average values of Precipitation Probability (Table)
dfma1 = df1.groupby(['year','month'])[df1h.columns[0:]].mean().sort_values(by = ['precipitation_probability','cloud_cover_mid',
                                        'soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
                                        'relative_humidity_2m','cloud_cover_high','dew_point_2m','vapour_pressure_deficit'], 
                                        ascending=[False,False,False,False,False,False,False,False,False,False])
dfma1

		precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
year	month
2025	05	8.831731	33.673077	0.287492	0.289117	74.913462	14.547957	0.671594	19.638878	39.136218	42.022436	8.303039	0.292835	18.513705	20.397869
	04	8.218056	28.886111	0.253883	0.259607	66.851389	10.539709	0.786940	17.725695	30.741667	33.441667	9.382421	0.269937	16.591750	18.325139
	03	5.859935	20.283388	0.243567	0.247840	54.768730	5.096467	0.880913	15.285845	18.687296	26.322476	11.941970	0.256166	14.110012	15.050236

# Find Monthly Average values of Precipitation Probability (Plot)
dfma2 = df1.groupby(['year','month'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].mean().sort_values(by = ['year', 'month'], ascending=[True,True])
# dfma2.plot(subplots=[('precipitation_probability', 'soil_moisture_0_to_1cm')])
dfma2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='year,month'>, <Axes: xlabel='year,month'>],
      dtype=object)

# Find Monthly Average values of Relative Humidity 2m (Table)
dfma3 = df1.groupby(['year','month'])[df1h.columns[[4,0]]].mean().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfma3

		relative_humidity_2m	precipitation_probability
year	month
2025	05	74.913462	8.831731
	04	66.851389	8.218056
	03	54.768730	5.859935

# Find Hourly Average values of Precipitation Probability (Table)
dfha1 = df1.groupby(['hmstz'])[df1h.columns[0:]].mean().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfha1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
hmstz
23:00:00+00:00	10.942029	28.840580	0.263391	0.268696	56.173913	10.882049	1.214140	21.308254	39.695652	35.289855	12.593755	0.274710	19.461949	21.080544
00:00:00+00:00	10.823529	34.941176	0.257765	0.262956	57.529412	10.437071	1.106596	20.361279	34.764706	29.632353	9.932043	0.271897	19.027412	19.146177
01:00:00+00:00	10.647059	30.588235	0.259250	0.263735	62.029412	10.544955	0.886789	18.910544	32.764706	28.338235	7.554958	0.271809	18.243589	17.395441
22:00:00+00:00	10.608696	29.768116	0.259449	0.265130	55.724638	11.495598	1.272770	22.011877	35.724638	38.695652	12.644852	0.274478	19.972819	22.882718
02:00:00+00:00	9.779412	28.205882	0.260721	0.265397	65.823529	10.718384	0.747160	17.927500	30.147059	23.455882	6.705819	0.273676	17.529618	16.485147
16:00:00+00:00	8.652174	27.115942	0.267812	0.271594	67.014493	12.405831	0.796096	19.478544	27.391304	52.550725	11.687352	0.278406	17.241660	22.940689
21:00:00+00:00	8.202899	31.347826	0.261406	0.265928	53.695652	11.403278	1.368297	22.540862	31.623188	40.405797	13.265757	0.275551	20.155428	24.279819
03:00:00+00:00	8.000000	30.205882	0.266338	0.268471	68.205882	10.751826	0.666444	17.269412	26.058824	25.338235	6.056407	0.275382	16.988441	15.791030
14:00:00+00:00	7.985507	30.666667	0.270246	0.272580	75.159420	11.900150	0.493985	16.782167	31.594203	46.028986	10.202882	0.279275	15.242384	18.747211
15:00:00+00:00	7.956522	30.405797	0.269029	0.271899	70.637681	12.203408	0.648221	18.226370	28.942029	47.202899	11.259874	0.279000	16.340935	21.092863
17:00:00+00:00	7.927536	25.231884	0.266000	0.270580	63.217391	12.284031	0.956532	20.465500	25.101449	45.637681	12.593281	0.277768	18.178616	24.290689
04:00:00+00:00	7.602941	30.308824	0.264985	0.268838	70.235294	10.564269	0.587748	16.516471	29.897059	31.102941	7.030144	0.276765	16.259765	15.252794
20:00:00+00:00	7.478261	25.449275	0.260623	0.267478	55.347826	11.879585	1.314835	22.433616	28.115942	42.855072	13.157123	0.276551	20.033689	25.377645
12:00:00+00:00	7.246377	34.782609	0.269493	0.271333	79.797101	10.539762	0.344758	14.289413	32.710145	44.449275	6.996893	0.276594	13.785863	14.058080
05:00:00+00:00	7.246377	28.579710	0.263609	0.267986	71.869565	10.431895	0.536891	15.989413	37.623188	26.246377	7.452990	0.276101	15.760500	14.785616
06:00:00+00:00	7.246377	25.246377	0.264725	0.268623	72.550725	10.280846	0.515896	15.645210	36.130435	25.869565	8.212435	0.276435	15.525718	14.474022
07:00:00+00:00	7.231884	27.057971	0.263739	0.267594	74.115942	10.231985	0.480893	15.270573	35.782609	16.521739	7.834606	0.275652	15.136587	14.150109
13:00:00+00:00	7.217391	31.362319	0.269696	0.272681	78.144928	11.263409	0.403967	15.429993	28.188406	44.086957	8.206373	0.278145	14.300355	16.387790
18:00:00+00:00	7.057971	30.768116	0.265739	0.271565	59.347826	12.180489	1.124769	21.453906	26.362319	46.420290	12.941720	0.277420	19.061225	25.222573
19:00:00+00:00	6.971014	28.231884	0.261986	0.268797	57.144928	12.093610	1.221855	22.024196	27.275362	46.275362	12.942607	0.277594	19.661950	25.821124
11:00:00+00:00	6.913043	29.318841	0.269942	0.271014	79.579710	10.094400	0.340336	13.862602	33.869565	35.463768	6.632991	0.276087	13.633689	13.237790
08:00:00+00:00	6.623188	31.565217	0.263420	0.267290	75.376812	10.176450	0.444812	14.887964	33.884058	20.826087	7.403544	0.275754	14.772819	13.934167
09:00:00+00:00	6.057971	23.231884	0.264681	0.267116	76.318841	10.044628	0.421411	14.601007	32.492754	26.275362	6.954408	0.274870	14.352529	13.645761
10:00:00+00:00	5.956522	25.202899	0.267768	0.268261	78.391304	10.179737	0.367218	14.210428	33.942029	29.130435	6.402455	0.274667	13.947457	13.387790

# Find Hourly Average values of Precipitation Probability (Plot)
dfha2 = df1.groupby(['hmstz'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].mean().sort_values(by = ['hmstz'], ascending=[True])
dfha2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='hmstz'>, <Axes: xlabel='hmstz'>], dtype=object)

# Find Hourly Average values of Relative Humidity 2m (Table)
dfha3 = df1.groupby(['hmstz'])[df1h.columns[[4,0]]].mean().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfha3

	relative_humidity_2m	precipitation_probability
hmstz
12:00:00+00:00	79.797101	7.246377
11:00:00+00:00	79.579710	6.913043
10:00:00+00:00	78.391304	5.956522
13:00:00+00:00	78.144928	7.217391
09:00:00+00:00	76.318841	6.057971
08:00:00+00:00	75.376812	6.623188
14:00:00+00:00	75.159420	7.985507
07:00:00+00:00	74.115942	7.231884
06:00:00+00:00	72.550725	7.246377
05:00:00+00:00	71.869565	7.246377
15:00:00+00:00	70.637681	7.956522
04:00:00+00:00	70.235294	7.602941
03:00:00+00:00	68.205882	8.000000
16:00:00+00:00	67.014493	8.652174
02:00:00+00:00	65.823529	9.779412
17:00:00+00:00	63.217391	7.927536
01:00:00+00:00	62.029412	10.647059
18:00:00+00:00	59.347826	7.057971
00:00:00+00:00	57.529412	10.823529
19:00:00+00:00	57.144928	6.971014
23:00:00+00:00	56.173913	10.942029
22:00:00+00:00	55.724638	10.608696
20:00:00+00:00	55.347826	7.478261
21:00:00+00:00	53.695652	8.202899

# Observation Result:
# - The fluctuation of average relative humidity and soil moisture values is relatively similar. Maybe, it's due to partial condensation
#   phenomenon, so when the precipitation probability is relatively stable (almost flat), the soil moisture is still fluctuating.
#   This phenomenon is used in Fog Farming to supply fresh water in arid/semi-arid area by using a lot of nets to help water vapour condensation.

# Find Daily Maximum of Precipitation Probability (Table)
dfdmax1 = df1.groupby('ymd')[df1h.columns[0:]].max().sort_values('precipitation_probability', ascending=False).head(5)
dfdmax1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
ymd
2025-04-06	100.0	100.0	0.427	0.432	100.0	17.785336	0.115238	18.6025	100.0	100.0	16.800856	0.440	19.6105	23.067501
2025-04-03	97.0	100.0	0.423	0.407	100.0	21.519682	2.017349	27.8025	100.0	100.0	30.248568	0.403	25.4605	25.167501
2025-05-21	97.0	100.0	0.402	0.401	96.0	19.394514	2.361591	26.8025	98.0	100.0	26.319422	0.384	23.1605	29.867500
2025-05-03	93.0	100.0	0.400	0.404	100.0	16.352500	0.292116	17.6525	100.0	100.0	21.862406	0.388	16.5105	21.867500
2025-03-31	90.0	100.0	0.390	0.393	96.0	18.642853	0.446352	20.9525	100.0	100.0	18.723460	0.363	20.8605	20.367500

# Find Daily Maximum of Precipitation Probability (Plot)
dfdmax2 = df1.groupby('ymd')[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].max().sort_values('ymd', ascending=True)
dfdmax2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='ymd'>, <Axes: xlabel='ymd'>], dtype=object)

# Find Daily Maximum of Relative Humidity 2m (Table)
dfdmax2 = df1.groupby('ymd')[df1h.columns[[4,0]]].max().sort_values('relative_humidity_2m', ascending=False).head(10)
dfdmax2

	relative_humidity_2m	precipitation_probability
ymd
2025-04-03	100.0	97.0
2025-05-08	100.0	59.0
2025-04-24	100.0	60.0
2025-05-03	100.0	93.0
2025-05-20	100.0	69.0
2025-04-06	100.0	100.0
2025-05-14	100.0	20.0
2025-05-06	100.0	1.0
2025-04-05	99.0	34.0
2025-05-13	99.0	50.0

# Find Monthly Maximum values of Precipitation Probability (Table)
dfmmax1 = df1.groupby(['year','month'])[df1h.columns[0:]].max().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmmax1

		precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
year	month
2025	04	100.0	100.0	0.427	0.432	100.0	22.277930	2.821701	30.6025	100.0	100.0	30.633865	0.440	27.560501	34.967500
	05	97.0	100.0	0.402	0.416	100.0	23.446577	3.207373	30.8525	100.0	100.0	27.103000	0.388	28.810501	34.367500
	03	90.0	100.0	0.399	0.402	96.0	19.002602	2.434383	26.8525	100.0	100.0	30.018473	0.393	23.760500	29.317501

# Find Monthly Maximum values of Precipitation Probability (Plot)
dfmmax2 = df1.groupby(['year','month'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].max().sort_values(by = ['year','month'], ascending=[True,True])
dfmmax2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='year,month'>, <Axes: xlabel='year,month'>],
      dtype=object)

# Find Monthly Maximum values of Relative Humidity 2m (Table)
dfmmax2 = df1.groupby(['year','month'])[df1h.columns[[4,0]]].max().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfmmax2

		relative_humidity_2m	precipitation_probability
year	month
2025	04	100.0	100.0
	05	100.0	97.0
	03	96.0	90.0

# Find Hourly Maximum values of Precipitation Probability (Table)
dfhmax1 = df1.groupby(['hmstz'])[df1h.columns[0:]].max().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfhmax1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
hmstz
06:00:00+00:00	100.0	100.0	0.417	0.416	97.0	19.825928	1.557914	25.0025	100.0	100.0	29.522196	0.412	25.010500	23.217500
07:00:00+00:00	100.0	100.0	0.406	0.404	99.0	19.963972	1.581724	25.8525	100.0	100.0	21.093050	0.398	25.210500	22.867500
05:00:00+00:00	100.0	100.0	0.396	0.393	96.0	19.922579	1.605850	26.0525	100.0	100.0	19.708150	0.393	23.860500	23.767500
09:00:00+00:00	100.0	100.0	0.390	0.395	97.0	20.332212	1.380920	24.8525	100.0	100.0	18.643690	0.406	24.510500	22.717500
08:00:00+00:00	99.0	100.0	0.427	0.432	100.0	19.970184	1.422268	25.1525	100.0	100.0	19.386593	0.440	24.960500	22.867500
00:00:00+00:00	97.0	100.0	0.402	0.401	97.0	20.266685	2.786320	28.8025	100.0	100.0	27.345785	0.384	25.910500	26.517500
11:00:00+00:00	97.0	100.0	0.378	0.385	98.0	20.572153	1.169993	24.4525	100.0	100.0	18.228943	0.362	23.960500	22.367500
03:00:00+00:00	95.0	100.0	0.398	0.396	95.0	20.345190	1.658432	27.5025	100.0	100.0	24.944130	0.394	25.160500	23.517500
18:00:00+00:00	93.0	100.0	0.386	0.416	98.0	21.859500	2.530884	29.8025	100.0	100.0	30.633865	0.372	26.960500	34.367500
14:00:00+00:00	92.0	100.0	0.413	0.411	100.0	21.660275	1.450493	25.1525	100.0	100.0	27.059933	0.381	23.910500	26.667501
17:00:00+00:00	92.0	100.0	0.375	0.376	100.0	22.353073	2.293195	28.6525	100.0	100.0	28.089230	0.387	25.810501	33.967500
12:00:00+00:00	91.0	100.0	0.383	0.384	100.0	20.832233	1.030431	24.3025	100.0	100.0	19.416653	0.377	23.860500	22.917501
04:00:00+00:00	90.0	100.0	0.394	0.391	97.0	21.105192	2.017349	27.8025	100.0	100.0	30.248568	0.393	24.410500	23.817501
10:00:00+00:00	90.0	100.0	0.377	0.362	98.0	20.350441	1.253686	24.6525	100.0	100.0	18.940240	0.376	24.360500	22.567501
13:00:00+00:00	89.0	100.0	0.401	0.393	100.0	21.262709	1.208841	24.9525	100.0	100.0	22.702845	0.380	23.760500	24.367500
02:00:00+00:00	87.0	100.0	0.400	0.402	100.0	20.394680	2.028199	27.0525	100.0	100.0	23.933908	0.383	25.460500	24.967500
15:00:00+00:00	86.0	100.0	0.406	0.405	100.0	21.586784	1.704031	26.6025	100.0	100.0	27.397867	0.419	24.010500	29.167501
19:00:00+00:00	86.0	100.0	0.384	0.386	96.0	23.203056	2.943742	30.4025	100.0	100.0	28.916763	0.384	28.810501	34.967500
22:00:00+00:00	86.0	100.0	0.373	0.353	98.0	22.441195	2.861490	29.8025	100.0	100.0	28.467329	0.366	27.860500	31.117500
01:00:00+00:00	72.0	100.0	0.386	0.371	100.0	20.926455	1.903188	27.3525	100.0	100.0	26.405453	0.376	25.610500	25.217500
20:00:00+00:00	71.0	100.0	0.423	0.407	96.0	22.576803	3.064757	30.8025	100.0	100.0	30.018473	0.403	28.760500	34.917500
16:00:00+00:00	70.0	100.0	0.400	0.404	100.0	23.446577	1.884407	27.8525	100.0	100.0	25.562534	0.388	24.910500	31.767500
21:00:00+00:00	64.0	100.0	0.383	0.385	96.0	22.452906	3.207373	30.8525	100.0	100.0	28.241076	0.394	28.460500	32.667500
23:00:00+00:00	59.0	100.0	0.395	0.385	95.0	21.758093	2.943496	29.5525	100.0	100.0	28.241076	0.366	26.210500	29.067501

# Find Hourly Maximum values of Precipitation Probability (Plot)
dfhmax2 = df1.groupby(['hmstz'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].max().sort_values(by = ['hmstz'], ascending=[True])
dfhmax2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='hmstz'>, <Axes: xlabel='hmstz'>], dtype=object)

# Find Hourly Maximum values of Relative Humidity 2m (Table)
dfhmax2 = df1.groupby(['hmstz'])[df1h.columns[[4,0]]].max().sort_values(by = ['relative_humidity_2m'], ascending=[False])
dfhmax2

	relative_humidity_2m	precipitation_probability
hmstz
12:00:00+00:00	100.0	91.0
14:00:00+00:00	100.0	92.0
02:00:00+00:00	100.0	87.0
17:00:00+00:00	100.0	92.0
16:00:00+00:00	100.0	70.0
08:00:00+00:00	100.0	99.0
15:00:00+00:00	100.0	86.0
01:00:00+00:00	100.0	72.0
13:00:00+00:00	100.0	89.0
07:00:00+00:00	99.0	100.0
22:00:00+00:00	98.0	86.0
18:00:00+00:00	98.0	93.0
10:00:00+00:00	98.0	90.0
11:00:00+00:00	98.0	97.0
00:00:00+00:00	97.0	97.0
09:00:00+00:00	97.0	100.0
06:00:00+00:00	97.0	100.0
04:00:00+00:00	97.0	90.0
05:00:00+00:00	96.0	100.0
19:00:00+00:00	96.0	86.0
20:00:00+00:00	96.0	71.0
21:00:00+00:00	96.0	64.0
03:00:00+00:00	95.0	95.0
23:00:00+00:00	95.0	59.0

# Observation Result:
# - When the maximum value of 'relative_humidity_2m' and 'precipitation_probability' decreases, the maximum value of moisture rises. Maybe, it's due to
#   the occurence of rain (humidity falls due to part of water vapour turns to liquid, and after that, the precipitation_probability falls also because
#   it's already release the water vapour as rain).

# Find Daily Minimum of Precipitation Probability (Table)
dfdmin1 = df1.groupby('ymd')[df1h.columns[0:]].min().sort_values(['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm'], ascending=True).head(5)
dfdmin1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
ymd
2025-04-21	0.0	0.0	0.148	0.167	40.0	12.389624	0.043685	18.5025	0.0	0.0	5.937272	0.195	17.4105	18.467500
2025-05-01	0.0	0.0	0.149	0.173	47.0	13.492453	0.634595	22.0025	0.0	0.0	5.091168	0.201	19.2105	20.417501
2025-04-20	0.0	0.0	0.154	0.176	38.0	13.304026	0.275796	17.2525	2.0	0.0	0.804984	0.201	18.9605	15.717500
2025-04-19	0.0	0.0	0.159	0.177	36.0	7.743205	0.634993	20.0025	0.0	0.0	4.452954	0.201	18.2105	16.917501
2025-04-18	0.0	0.0	0.171	0.189	26.0	3.101421	0.959298	18.1525	0.0	0.0	4.024922	0.212	16.6105	15.117499

# Find Daily Minimum of Precipitation Probability (Plot)
dfdmin2 = df1.groupby('ymd')[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].min().sort_values('ymd', ascending=True)
dfdmin2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='ymd'>, <Axes: xlabel='ymd'>], dtype=object)

# Find Daily Minimum of relative_humidity_2m (Table)
dfdmin3 = df1.groupby('ymd')[df1h.columns[[4,0]]].min().sort_values(['relative_humidity_2m'], ascending=True).head(5)
dfdmin3

	relative_humidity_2m	precipitation_probability
ymd
2025-04-16	19.0	0.0
2025-04-10	20.0	0.0
2025-04-09	21.0	0.0
2025-04-17	21.0	0.0
2025-03-21	22.0	0.0

# Observation Result of Daily Minimum Data
# - Fluctuation of 'relative_humidity_2m' is similar to 'soil_moisture_0_to_1cm', different compared to Hourly Minimum Data, 
#   maybe due to longer time scope.

# Find Monthly Minimum values of Precipitation Probability (Table)
dfmmin1 = df1.groupby(['year','month'])[df1h.columns[0:]].min().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[True,True,True,True,True,True,True,True,True,True])
dfmmin1

		precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
year	month
2025	04	0.0	0.0	0.148	0.167	19.0	-7.360636	0.000000	0.9025	0.0	0.0	0.360000	0.195	0.7605	0.3675
	05	0.0	0.0	0.149	0.173	28.0	6.472079	0.000000	8.1525	0.0	0.0	0.360000	0.201	7.1605	7.4675
	03	0.0	0.0	0.178	0.188	22.0	-6.138609	0.047987	-2.7975	0.0	0.0	1.018234	0.206	2.6605	-3.0825

# Find Monthly Minimum values of Precipitation Probability (Plot)
dfmmin2 = df1.groupby(['year','month'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].min().sort_values(by = ['year','month'], ascending=[True,True])
dfmmin2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='year,month'>, <Axes: xlabel='year,month'>],
      dtype=object)

# Find Monthly Minimum values of Relative Humidity 2m (Table)
dfmmin2 = df1.groupby(['year','month'])[df1h.columns[[4,0]]].min().sort_values(by = ['relative_humidity_2m'], ascending=[True])
dfmmin2

		relative_humidity_2m	precipitation_probability
year	month
2025	04	19.0	0.0
	03	22.0	0.0
	05	28.0	0.0

# Find Hourly Minimum values of Precipitation Probability (Table)
dfhmin1 = df1.groupby(['hmstz'])[df1h.columns[0:]].min().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[True,True,True,True,True,True,True,True,True,True])
dfhmin1

	precipitation_probability	cloud_cover_mid	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	relative_humidity_2m	dew_point_2m	vapour_pressure_deficit	temperature_2m	cloud_cover_high	cloud_cover_low	wind_speed_10m	soil_moisture_3_to_9cm	temperature_80m	soil_temperature_0cm
hmstz
04:00:00+00:00	0.0	0.0	0.148	0.168	30.0	-5.247747	5.270660e-02	3.2525	0.0	0.0	1.138420	0.196	4.0605	2.5175
03:00:00+00:00	0.0	0.0	0.148	0.169	31.0	-5.802887	6.925583e-02	3.5525	0.0	0.0	0.804984	0.196	4.6605	3.2175
02:00:00+00:00	0.0	0.0	0.148	0.169	31.0	-5.230092	0.000000e+00	4.2025	0.0	0.0	1.484318	0.196	5.3105	4.1175
01:00:00+00:00	0.0	0.0	0.148	0.170	28.0	-7.360636	1.192093e-07	5.2025	0.0	0.0	1.297998	0.197	6.1105	5.3675
00:00:00+00:00	0.0	0.0	0.148	0.171	21.0	-6.458873	3.803062e-02	6.3025	0.0	0.0	0.360000	0.197	6.3105	6.3675
07:00:00+00:00	0.0	0.0	0.149	0.167	30.0	-5.318303	1.790810e-02	-0.0975	0.0	0.0	1.080000	0.195	3.2605	0.1675
06:00:00+00:00	0.0	0.0	0.149	0.168	31.0	-4.884931	5.088019e-02	1.0025	0.0	0.0	1.938659	0.195	3.5105	0.2675
05:00:00+00:00	0.0	0.0	0.149	0.168	31.0	-5.715442	6.960440e-02	1.6025	0.0	0.0	1.527351	0.196	3.6105	1.8175
08:00:00+00:00	0.0	0.0	0.150	0.167	30.0	-5.057073	0.000000e+00	-0.6475	0.0	0.0	0.360000	0.195	2.9605	0.0175
23:00:00+00:00	0.0	0.0	0.153	0.176	19.0	-7.046234	9.362638e-02	6.6025	0.0	0.0	2.545584	0.201	5.9105	7.7675
22:00:00+00:00	0.0	0.0	0.154	0.177	19.0	-5.147153	3.865421e-02	6.3525	0.0	0.0	0.360000	0.202	5.0605	8.0675
21:00:00+00:00	0.0	0.0	0.157	0.178	19.0	-6.268236	5.508649e-02	6.3025	0.0	0.0	0.509117	0.201	4.8105	8.9175
09:00:00+00:00	0.0	0.0	0.158	0.168	28.0	-6.138609	4.315126e-02	-1.2975	0.0	0.0	0.360000	0.195	2.8105	-0.1325
10:00:00+00:00	0.0	0.0	0.159	0.173	32.0	-5.867736	3.780699e-02	-1.4975	0.0	0.0	1.018234	0.195	2.5605	-0.2825
11:00:00+00:00	0.0	0.0	0.160	0.174	33.0	-6.123685	3.316426e-02	-1.9975	0.0	0.0	1.297998	0.197	1.3105	-0.4825
20:00:00+00:00	0.0	0.0	0.160	0.174	20.0	-5.321992	5.711555e-02	5.5025	0.0	0.0	0.360000	0.201	4.7605	8.9175
14:00:00+00:00	0.0	0.0	0.161	0.174	33.0	-3.707861	2.384186e-07	3.7025	0.0	0.0	0.509117	0.204	1.6605	4.6175
12:00:00+00:00	0.0	0.0	0.161	0.174	36.0	-4.933545	0.000000e+00	-2.7975	0.0	0.0	0.804984	0.204	0.7605	-3.0825
13:00:00+00:00	0.0	0.0	0.162	0.174	35.0	-2.786416	1.192093e-07	-0.2975	0.0	0.0	0.720000	0.204	0.7605	-0.6325
19:00:00+00:00	0.0	0.0	0.163	0.174	21.0	-5.242775	6.885850e-02	5.9025	0.0	0.0	3.396233	0.202	4.5105	9.9175
15:00:00+00:00	0.0	0.0	0.164	0.174	30.0	-3.815094	0.000000e+00	5.2525	0.0	0.0	0.360000	0.203	2.8105	7.3175
18:00:00+00:00	0.0	0.0	0.167	0.174	24.0	-4.048950	4.450750e-02	5.9525	0.0	0.0	1.609969	0.202	4.6105	9.2675
17:00:00+00:00	0.0	0.0	0.170	0.175	23.0	-4.052588	0.000000e+00	6.5525	0.0	0.0	0.509117	0.202	5.0105	10.0675
16:00:00+00:00	0.0	0.0	0.172	0.175	26.0	-4.160234	0.000000e+00	5.4025	0.0	0.0	2.036468	0.203	4.0105	8.4675

# Find Hourly Minimum values of Precipitation Probability (Plot)
dfhmin2 = df1.groupby(['hmstz'])[['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','relative_humidity_2m']].min().sort_values(by = ['hmstz'], ascending=[True])
dfhmin2.plot(subplots=[('precipitation_probability','cloud_cover_mid','relative_humidity_2m'),('soil_moisture_0_to_1cm','soil_moisture_1_to_3cm')])

array([<Axes: xlabel='hmstz'>, <Axes: xlabel='hmstz'>], dtype=object)

# Find Hourly Minimum values of Relative Humidity 2m (Table)
dfhmin2 = df1.groupby(['hmstz'])[df1h.columns[[4,0]]].min().sort_values(by = ['relative_humidity_2m'], ascending=[True])
dfhmin2

	relative_humidity_2m	precipitation_probability
hmstz
23:00:00+00:00	19.0	0.0
21:00:00+00:00	19.0	0.0
22:00:00+00:00	19.0	0.0
20:00:00+00:00	20.0	0.0
19:00:00+00:00	21.0	0.0
00:00:00+00:00	21.0	0.0
17:00:00+00:00	23.0	0.0
18:00:00+00:00	24.0	0.0
16:00:00+00:00	26.0	0.0
09:00:00+00:00	28.0	0.0
01:00:00+00:00	28.0	0.0
07:00:00+00:00	30.0	0.0
08:00:00+00:00	30.0	0.0
04:00:00+00:00	30.0	0.0
15:00:00+00:00	30.0	0.0
06:00:00+00:00	31.0	0.0
02:00:00+00:00	31.0	0.0
05:00:00+00:00	31.0	0.0
03:00:00+00:00	31.0	0.0
10:00:00+00:00	32.0	0.0
14:00:00+00:00	33.0	0.0
11:00:00+00:00	33.0	0.0
13:00:00+00:00	35.0	0.0
12:00:00+00:00	36.0	0.0

# Observation result:
# - When the minimum value of 'relative_humidity_2m' rises, the min value of 'soil_moisture_0_to_1cm' doesn't immediately rise, but instead, steadily almost flat. Maybe, it's due to part of liquid water in the soil vaporize and change into water vapour, so increase the relative humidity of air (near the soil).
# - When 'relative_humidity_2m' falls, the value of 'soil_moisture_0_to_1cm' doesn't immediately falls, but instead, in contrary, rises big enough. Maybe, it's due to part of water vapour in the air near the soil condensates and add liquid water in the soil so the soil moisture increases.

Mode value of a Column (for sample, we take 'temperature_2m' Column)

# Find Mode value if not rounded
dfmod = df1.mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod = dfmod.dropna()
dfmod
# The result is 17.4525 if we don't do any number rounding. But this result in the most cases, maybe is not the best mode at all.

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	581	2025-03-19 05:00:00+00:00	17.4525	92.0	15.176124	17.777193	0.0	0.0	0.0	0.0	1017.5	990.0348	100.0	0.0	0.0	0.0	12200.0	3.096837	0.265	0.268	0.283	0.274	0.288	0.0	0.0	0.0	0.0	0.0	8.311245	13.991941	17.654688	180.0	180.0	180.0	180.0	16.1605	16.4605	17.3675	17.9675	19.4675	17.067501	14.1675	17.64	Nashville	2025-03-20	05:00:00+00:00	2025	04	20	05	00	00+00	00

dfmoda = df1[(df1['temperature_2m'] >= 17.45245) &  (df1['temperature_2m'] < 17.45255)] #.where(df1['temperature_2m'] == 19)
dfmoda = dfmoda.dropna()
print(dfmoda['temperature_2m'])
len(dfmoda)
# The result: the temperature of 17.4525 only occurred 11 times out of 1651 data samples.

920     17.4525
1354    17.4525
1396    17.4525
1491    17.4525
1530    17.4525
1768    17.4525
1777    17.4525
1802    17.4525
1900    17.4525
2117    17.4525
2223    17.4525
Name: temperature_2m, dtype: float64

11

# Find Mode value precise to 0 decimal number
dfmod0 = df1.round(0).mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod0 = dfmod0.dropna()
dfmod0
# The result is 19 if we round it to the nearest integer (0 decimal number)

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	581	2025-03-19 05:00:00+00:00	19.0	92.0	16.0	19.0	0.0	0.0	0.0	0.0	1018.0	1000.0	100.0	0.0	0.0	0.0	12200.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	13.0	20.0	19.0	180.0	176.0	185.0	200.0	16.0	16.0	17.0	18.0	19.0	20.0	17.0	10.0	Nashville	2025-03-20	05:00:00+00:00	2025	04	20	05	00	00+00	00

# Find out how many occurrences of 'temperature_2m' rounded to 19C (0 decimal number)
dfmod0a = df1[(df1['temperature_2m'] >= 18.5) &  (df1['temperature_2m'] < 19.5)]
dfmod0a = dfmod0a.dropna()
print(dfmod0a['temperature_2m'])
# The result: temperature of 19C occurred 127 times out of 1651 data samples.
# This is far more occurences than if we use the 17.4525C (4 decimal numbers)

596     19.0995
597     19.2995
600     18.9995
666     18.7525
695     19.1025
         ...   
2198    19.0525
2205    19.2025
2208    19.4525
2209    18.8525
2225    19.0025
Name: temperature_2m, Length: 127, dtype: float64

# Find Mode value of Precipitation Probability (Table) precise to 1 decimal number
dfmod1 = df1.round(1).mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod1 = dfmod1.dropna()
dfmod1

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	581	2025-03-19 05:00:00+00:00	19.7	92.0	15.6	16.2	0.0	0.0	0.0	0.0	1017.5	998.8	100.0	0.0	0.0	0.0	12200.0	5.2	0.3	0.3	0.3	0.3	0.3	0.0	0.0	0.0	0.0	0.2	17.3	26.5	17.7	180.0	180.0	180.0	180.0	16.2	17.5	17.4	18.0	19.5	20.2	14.2	17.6	Nashville	2025-03-20	05:00:00+00:00	2025	04	20	05	00	00+00	00

# Find out how many occurrences of 'temperature_2m' rounded to 19.7C (1 decimal number)
dfmod1a = df1[(df1['temperature_2m'] >= 19.65) &  (df1['temperature_2m'] < 19.75)] #.where(df1['temperature_2m'] == 19)
dfmod1a = dfmod1a.dropna()
print(dfmod1a['temperature_2m'])
len(dfmod1a)
# The result: the temperature of 19.7C occurred only 18 times out of 1651 data samples.

828     19.6525
909     19.6525
1119    19.6525
1272    19.6525
1299    19.7025
1430    19.6525
1468    19.6525
1469    19.7025
1484    19.7025
1486    19.6525
1512    19.7025
1637    19.7025
1651    19.6525
1791    19.7025
1874    19.7025
1875    19.6525
2048    19.7025
2089    19.6525
Name: temperature_2m, dtype: float64

18

# Find Mode value of Precipitation Probability (Table) precise to 2 decimal numbers
dfmod2 = df1.round(2).mode().sort_values(by = ['precipitation_probability','cloud_cover_mid','soil_moisture_0_to_1cm','soil_moisture_1_to_3cm','soil_moisture_3_to_9cm','cloud_cover_low',
            'relative_humidity_2m','cloud_cover_high','dew_point_2m',
            'vapour_pressure_deficit'], ascending=[False,False,False,False,False,False,False,False,False,False])
dfmod2 = dfmod2.dropna()
dfmod2

	Unnamed: 0	date	temperature_2m	relative_humidity_2m	dew_point_2m	apparent_temperature	precipitation_probability	precipitation	rain	showers	pressure_msl	surface_pressure	cloud_cover	cloud_cover_low	cloud_cover_mid	cloud_cover_high	visibility	wind_speed_10m	soil_moisture_0_to_1cm	soil_moisture_1_to_3cm	soil_moisture_3_to_9cm	soil_moisture_9_to_27cm	soil_moisture_27_to_81cm	evapotranspiration	snowfall	snow_depth	et0_fao_evapotranspiration	vapour_pressure_deficit	wind_speed_80m	wind_speed_120m	wind_speed_180m	wind_direction_10m	wind_direction_80m	wind_direction_120m	wind_direction_180m	temperature_80m	temperature_120m	temperature_180m	soil_temperature_0cm	soil_temperature_6cm	soil_temperature_18cm	soil_temperature_54cm	wind_gusts_10m	Location	ymd	hmstz	year	month	day	hour	minute	sectz	tzsec
0	581	2025-03-19 05:00:00+00:00	17.45	92.0	8.75	16.18	0.0	0.0	0.0	0.0	1017.5	998.63	100.0	0.0	0.0	0.0	12200.0	3.1	0.28	0.24	0.28	0.28	0.29	0.0	0.0	0.0	0.0	0.15	8.31	19.11	17.65	180.0	180.0	180.0	180.0	16.16	16.46	17.37	17.97	19.47	17.07	14.17	17.64	Nashville	2025-03-20	05:00:00+00:00	2025	04	20	05	00	00+00	00

# Find out how many occurrences of 'temperature_2m' rounded to 17.45C (2 decimal numbers)
dfmod2a = df1[(df1['temperature_2m'] >= 17.445) &  (df1['temperature_2m'] < 17.455)] #.where(df1['temperature_2m'] == 19)
dfmod2a = dfmod2a.dropna()
print(dfmod2a['temperature_2m'])
len(dfmod2a)
# The result: the temperature of 17.45C occurred only 11 times out of 1651 data samples. This is perfectly the same as if we don't round it.

920     17.4525
1354    17.4525
1396    17.4525
1491    17.4525
1530    17.4525
1768    17.4525
1777    17.4525
1802    17.4525
1900    17.4525
2117    17.4525
2223    17.4525
Name: temperature_2m, dtype: float64

11

# CONCLUSION: to get the nearest possible Mode value of a Column (in this sample, we take 'temperature_2m'), we can't just take the real values
# but, instead, maybe we have to round it to the nearest integer/whole number.

Standard Deviation and Outliers of Precipitation Probability

# Function Definition
def find_outliers_iqr(series):
       Q1 = series.quantile(0.25)
       print('Q1 = ', Q1)
       Q2 = series.quantile(0.50)
       print('Q2 = ', Q2)
       Q3 = series.quantile(0.75)
       print('Q3 = ', Q3)
       IQR = Q3 - Q1
       lower_bound = Q1 - 1.5 * IQR
       print('Lower Bound = ', lower_bound)
       upper_bound = Q3 + 1.5 * IQR
       print('Upper Bound = ', upper_bound)
       outliers = series[(series < lower_bound) | (series > upper_bound)]
       return outliers

##### Standard Deviation of 'precipitation_probability'
df1stdevpp = df1[['precipitation_probability']].std() #.sort_values(ascending=False)
df1stdevpp

precipitation_probability    16.726911
dtype: float64

df1[['precipitation_probability']].mean()

precipitation_probability    8.011508
dtype: float64

# Distribution Plot of 'precipitation_probability'
df1[['precipitation_probability']].plot.kde()

<Axes: ylabel='Density'>

##### Outliers of 'precipitation_probability'
# Quantiles of 'precipitation_probability'
# Note: either Lower Bound and/or Upper Bound doesn't have to be exist at all in the Dataset.
find_outliers_iqr(df1[['precipitation_probability']]).dropna().sort_values(by='precipitation_probability', ascending=False)

Q1 =  precipitation_probability    0.0
Name: 0.25, dtype: float64
Q2 =  precipitation_probability    1.0
Name: 0.5, dtype: float64
Q3 =  precipitation_probability    7.0
Name: 0.75, dtype: float64
Lower Bound =  precipitation_probability   -10.5
dtype: float64
Upper Bound =  precipitation_probability    17.5
dtype: float64

	precipitation_probability
1015	100.0
1013	100.0
1017	100.0
1014	100.0
1016	99.0
...	...
847	18.0
1034	18.0
862	18.0
1035	18.0
693	18.0

239 rows × 1 columns

# Box Plot of 'precipitation_probability'
# Note:
# Top black horizontal line is Upper Bound value
# Bottom black horizontal line is Lower Bound value
# Lower blue line is 25 percentile
# Green line is Median a.k.a 50 percentile
# Upper blue line is 75 percentile
df1[['precipitation_probability']].boxplot()

<Axes: >

# Draw the Scatter Plot of 'precipitation_probability'
# We can see that the dots are concentrated at below/bottom (below the Upper Bound of 17.5)
import matplotlib.pyplot as plt
df1[['date','precipitation_probability']].plot.scatter(x='date', y='precipitation_probability', s=1, c='green')

<Axes: xlabel='date', ylabel='precipitation_probability'>

##### Standard Deviation of 'temperature_2m'
df1stdevtemp = df1[['temperature_2m']].std() #.sort_values(ascending=False)
df1stdevtemp

temperature_2m    6.09753
dtype: float64

df1[['temperature_2m']].mean()

temperature_2m    17.995103
dtype: float64

df1[['temperature_2m']].max()

temperature_2m    30.8525
dtype: float64

df1[['temperature_2m']].plot.kde()

<Axes: ylabel='Density'>

##### Outliers of 'temperature_2m'
# Quantiles of 'temperature_2m'
# Note: either Lower Bound and/or Upper Bound doesn't have to be exist at all in the Dataset.
outlierstemp = find_outliers_iqr(df1[['temperature_2m']])
print(outlierstemp.dropna().sort_values(by='temperature_2m', ascending=False))

Q1 =  temperature_2m    14.252501
Name: 0.25, dtype: float64
Q2 =  temperature_2m    18.5525
Name: 0.5, dtype: float64
Q3 =  temperature_2m    22.2025
Name: 0.75, dtype: float64
Lower Bound =  temperature_2m    2.327502
dtype: float64
Upper Bound =  temperature_2m    34.127499
dtype: float64
      temperature_2m
1087          2.3025
1088          1.8525
629           1.6025
1089          1.4525
1092          1.3525
630           1.0025
1090          1.0025
1091          0.9025
631          -0.0975
637          -0.2975
632          -0.6475
633          -1.2975
634          -1.4975
635          -1.9975
636          -2.7975

# Box Plot of 'temperature_2m'
# Note:
# Top black horizontal line is Upper Bound value
# Bottom black horizontal line is Lower Bound value
# Lower blue line is 25 percentile
# Green line is Median a.k.a 50 percentile
# Upper blue line is 75 percentile
df1[['temperature_2m']].boxplot()

<Axes: >

# Draw the Scatter Plot of 'temperature_2m'
# We can see that the dots are concentrated at below/bottom (below the Upper Bound of 17.5)
import matplotlib.pyplot as plt
df1[['date','temperature_2m']].plot.scatter(x='date', y='temperature_2m', s=1, c='green')

<Axes: xlabel='date', ylabel='temperature_2m'>

# Boxplot of all Columns
df1[df1.columns[1:9]].boxplot()

<Axes: >

df1[df1.columns[9:17]].boxplot()

<Axes: >

df1[df1.columns[17:26]].boxplot()

<Axes: >

df1[df1.columns[26:35]].boxplot()

<Axes: >

df1[df1.columns[35:]].boxplot()

<Axes: >